{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5a1368f0",
   "metadata": {},
   "source": [
    "# 作业4：卷积神经网络实践"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "294a04c2",
   "metadata": {},
   "source": [
    "本次作业将练习卷积神经网络，利用卷积层和全连接层实现手写数字的识别。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9ee659c",
   "metadata": {},
   "source": [
    "## 1. 目标"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "997082ed",
   "metadata": {},
   "source": [
    "通过对 MNIST 数据进行训练，构建一个简单的图像分类模型，对图片中的数字进行识别。你将利用该模型对自己真实手写出的数字进行预测，观察模型效果。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0490f78e",
   "metadata": {},
   "source": [
    "## 2. 主要步骤"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b786836",
   "metadata": {},
   "source": [
    "1. 获取数据\n",
    "2. 定义模型结构\n",
    "3. 创建模型类\n",
    "4. 定义损失函数\n",
    "5. 编写训练循环\n",
    "6. 实施预测"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45cd7d88",
   "metadata": {},
   "source": [
    "### 2.1 获取数据"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccc63241",
   "metadata": {},
   "source": [
    "我们使用知名的 MNIST 数据集，它可以从 PyTorch 中利用工具函数下载得到。MNIST 数据训练集大小为60000，我们将**使用完整训练集进行训练**，并对10个测试集观测进行预测展示。以下函数会在当前目录建立一个名为 data 的文件夹，其中会包含下载得到的数据集。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2069eef6",
   "metadata": {},
   "source": [
    "**注意：请在任何程序的最开始加上随机数种子的设置。请保持这一习惯。**"
   ]
  },
  {
   "cell_type": "code",
   "id": "57301cf0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-07T09:26:31.632667Z",
     "start_time": "2025-05-07T09:26:05.712227Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "from torchvision import datasets\n",
    "from torchvision.transforms import ToTensor\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "np.random.seed(123456)\n",
    "torch.manual_seed(123456)\n",
    "\n",
    "mnist = datasets.MNIST(\n",
    "    root=\"data\",\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform=ToTensor()\n",
    ")\n",
    "loader = DataLoader(mnist, batch_size=60000, shuffle=True)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
      "Failed to download (trying next):\n",
      "HTTP Error 404: Not Found\n",
      "\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to data\\MNIST\\raw\\train-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 9.91M/9.91M [00:05<00:00, 1.84MB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting data\\MNIST\\raw\\train-images-idx3-ubyte.gz to data\\MNIST\\raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
      "Failed to download (trying next):\n",
      "HTTP Error 404: Not Found\n",
      "\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to data\\MNIST\\raw\\train-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 28.9k/28.9k [00:00<00:00, 118kB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting data\\MNIST\\raw\\train-labels-idx1-ubyte.gz to data\\MNIST\\raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
      "Failed to download (trying next):\n",
      "HTTP Error 404: Not Found\n",
      "\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to data\\MNIST\\raw\\t10k-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1.65M/1.65M [00:01<00:00, 1.05MB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting data\\MNIST\\raw\\t10k-images-idx3-ubyte.gz to data\\MNIST\\raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
      "Failed to download (trying next):\n",
      "HTTP Error 404: Not Found\n",
      "\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to data\\MNIST\\raw\\t10k-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 4.54k/4.54k [00:00<?, ?B/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting data\\MNIST\\raw\\t10k-labels-idx1-ubyte.gz to data\\MNIST\\raw\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "id": "afdaba44",
   "metadata": {},
   "source": [
    "我们一次性取出60000个观测，其中 x 是图片数据，y 是图片对应的数字。"
   ]
  },
  {
   "cell_type": "code",
   "id": "61ae2977",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-07T09:28:40.802087Z",
     "start_time": "2025-05-07T09:28:37.812636Z"
    }
   },
   "source": [
    "x, y = next(iter(loader))"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "id": "145971d1",
   "metadata": {},
   "source": [
    "一个习惯性动作是查看数据的大小和维度。"
   ]
  },
  {
   "cell_type": "code",
   "id": "d67073f6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-07T09:28:44.864701Z",
     "start_time": "2025-05-07T09:28:44.861192Z"
    }
   },
   "source": [
    "print(x.shape)\n",
    "print(y.shape)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([60000, 1, 28, 28])\n",
      "torch.Size([60000])\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "id": "8fbb19ee",
   "metadata": {},
   "source": [
    "用类似的方法获取测试集，并取出10个观测："
   ]
  },
  {
   "cell_type": "code",
   "id": "25db4998",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-07T09:28:50.404781Z",
     "start_time": "2025-05-07T09:28:50.390533Z"
    }
   },
   "source": [
    "mnist_test = datasets.MNIST(\n",
    "    root=\"data\",\n",
    "    train=False,\n",
    "    download=True,\n",
    "    transform=ToTensor()\n",
    ")\n",
    "loader = DataLoader(mnist_test, batch_size=10, shuffle=True)\n",
    "\n",
    "xtest, ytest = next(iter(loader))\n",
    "print(xtest.shape)\n",
    "print(ytest.shape)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([10, 1, 28, 28])\n",
      "torch.Size([10])\n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "id": "f8a0aa24",
   "metadata": {},
   "source": [
    "我们可以利用下面的函数展示图片的内容。如选择第一张测试图片，先将其转换成 Numpy 数组，再绘制图形："
   ]
  },
  {
   "cell_type": "code",
   "id": "c2de2307",
   "metadata": {
    "scrolled": true,
    "ExecuteTime": {
     "end_time": "2025-05-07T09:28:55.191546Z",
     "start_time": "2025-05-07T09:28:54.536946Z"
    }
   },
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "img = xtest[0].squeeze().cpu().numpy()\n",
    "print(img.shape)\n",
    "plt.imshow(img, cmap=\"gray\")\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "id": "6ad3bff1",
   "metadata": {},
   "source": [
    "接下来请你选择5个你喜欢的数字（60000以下），然后取出训练集中对应位置的图片，并画出它们的内容。"
   ]
  },
  {
   "cell_type": "code",
   "id": "49728eff",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-07T09:31:24.230659Z",
     "start_time": "2025-05-07T09:31:24.078954Z"
    }
   },
   "source": [
    "\n",
    "np.random.seed(123456)\n",
    "torch.manual_seed(123456)\n",
    "\n",
    "mnist = datasets.MNIST(\n",
    "    root=\"data\",\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform=ToTensor()\n",
    ")\n",
    "\n",
    "indices = [123, 4567, 2004, 1224, 425]  \n",
    "images = [mnist[i][0] for i in indices]\n",
    "labels = [mnist[i][1] for i in indices]\n",
    "\n",
    "plt.figure(figsize=(10, 2))\n",
    "for i in range(5):\n",
    "    plt.subplot(1, 5, i + 1)\n",
    "    plt.imshow(images[i].squeeze(), cmap=\"gray\")\n",
    "    plt.title(f\"Label: {labels[i]}\")\n",
    "    plt.axis(\"off\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x200 with 5 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "id": "0281c1d2",
   "metadata": {},
   "source": [
    "### 2.2 定义模型结构"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35776058",
   "metadata": {},
   "source": [
    "我们搭建一个类似于 LeNet-5 的网络，结构如下："
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d175cb17",
   "metadata": {},
   "source": [
    "![](https://pic1.zhimg.com/80/v2-82eabb4c17e90d467197d013f7629f3c_720w.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ec06760",
   "metadata": {},
   "source": [
    "我们需要创建2个卷积层、2个汇聚层和2个全连接层，**暂时忽略所有的激活函数**。所有隐藏层的函数细节都可以在[官方文档](https://pytorch.org/docs/stable/nn.html)中按分类找到。每一个隐藏层本质上都是将一个数组变换成另一个数组的函数，因此为了确认编写的模型是正确的，可以先用一个小数据进行测试，观察输入和输出的维度。例如，我们先取出前6个观测，此时输入的维度是 `[6, 1, 28, 28]`："
   ]
  },
  {
   "cell_type": "code",
   "id": "a26ebf44",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-07T09:33:00.009840Z",
     "start_time": "2025-05-07T09:33:00.006742Z"
    }
   },
   "source": [
    "ns = 6\n",
    "smallx = x[0:ns]\n",
    "smally = y[0:ns]\n",
    "print(smallx.shape)\n",
    "print(smally.shape)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([6, 1, 28, 28])\n",
      "torch.Size([6])\n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "id": "ada326cb",
   "metadata": {},
   "source": [
    "接下来创建第1个卷积层，并测试输出的维度。注意到我们可以直接将隐藏层当成一个函数来调用。"
   ]
  },
  {
   "cell_type": "code",
   "id": "500c1874",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-07T09:33:17.592438Z",
     "start_time": "2025-05-07T09:33:17.551680Z"
    }
   },
   "source": [
    "conv1 = torch.nn.Conv2d(in_channels=1, out_channels=20, kernel_size=5, stride=1)\n",
    "res1 = conv1(smallx)\n",
    "print(res1.shape)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([6, 20, 24, 24])\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "id": "b2e8230d",
   "metadata": {},
   "source": [
    "可以看到，输出的维度为 `[20, 24, 24]`（不包括第1位的数据批次维度），与之前图中的结果吻合。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfc3c891",
   "metadata": {},
   "source": [
    "接下来，请按照图中提示编写层对象 `pool1`、`conv2`、`pool2`、`fc1` 和 `fc2`，并顺次测试输入与输出的维度，使其与上图匹配。注意，在将一个大小为 `[6, 50, 4, 4]` 的数组（假设叫 `somearray`）传递给 `fc1` 之前，需要先将其变形为只有两个维度的数组，做法是 `somearray.view(-1, 50 * 4 * 4)`，其中 -1 表示该位置的大小不变。也可以使用 `torch.flatten()` 函数并指定其中的 `start_dim` 参数（请搜索其对应的函数文档）。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94e3f58c",
   "metadata": {},
   "source": [
    "```py\n",
    "pool1 = ...\n",
    "res2 = pool1(res1)\n",
    "print(res2.shape)\n",
    "assert res2.shape == (ns, 20, 12, 12), \"pool1 输出形状不对\"\n",
    "\n",
    "conv2 = ...\n",
    "res3 = conv2(res2)\n",
    "print(res3.shape)\n",
    "assert res3.shape == (ns, 50, 8, 8), \"conv2 输出形状不对\"\n",
    "\n",
    "pool2 = ...\n",
    "res4 = pool2(res3)\n",
    "print(res4.shape)\n",
    "assert res4.shape == (ns, 50, 4, 4), \"pool2 输出形状不对\"\n",
    "\n",
    "fc1 = ...\n",
    "res5 = fc1(res4.view(-1, 800))\n",
    "print(res5.shape)\n",
    "assert res5.shape == (ns, 500), \"fc1 输出形状不对\"\n",
    "\n",
    "fc2 = ...\n",
    "res6 = fc2(res5)\n",
    "print(res6.shape)\n",
    "assert res6.shape == (ns, 10), \"fc2 输出形状不对\"\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "id": "89164f79",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-07T09:37:41.257967Z",
     "start_time": "2025-05-07T09:37:41.212838Z"
    }
   },
   "source": [
    "import torch.nn as nn\n",
    "pool1 = nn.MaxPool2d(kernel_size=2, stride=2)\n",
    "res2 = pool1(res1)\n",
    "print(res2.shape)\n",
    "assert res2.shape == (ns, 20, 12, 12), \"pool1 输出形状不对\"\n",
    "\n",
    "conv2 = nn.Conv2d(in_channels=20, out_channels=50, kernel_size=5, stride=1)\n",
    "res3 = conv2(res2)\n",
    "print(res3.shape)\n",
    "assert res3.shape == (ns, 50, 8, 8), \"conv2 输出形状不对\"\n",
    "\n",
    "pool2 = nn.MaxPool2d(kernel_size=2, stride=2)\n",
    "res4 = pool2(res3)\n",
    "print(res4.shape)\n",
    "assert res4.shape == (ns, 50, 4, 4), \"pool2 输出形状不对\"\n",
    "\n",
    "fc1 = nn.Linear(800, 500)\n",
    "res5 = fc1(res4.view(-1, 800))\n",
    "print(res5.shape)\n",
    "assert res5.shape == (ns, 500), \"fc1 输出形状不对\"\n",
    "\n",
    "fc2 = nn.Linear(500, 10)\n",
    "res6 = fc2(res5)\n",
    "print(res6.shape)\n",
    "assert res6.shape == (ns, 10), \"fc2 输出形状不对\""
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([6, 20, 12, 12])\n",
      "torch.Size([6, 50, 8, 8])\n",
      "torch.Size([6, 50, 4, 4])\n",
      "torch.Size([6, 500])\n",
      "torch.Size([6, 10])\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "markdown",
   "id": "f623e96f",
   "metadata": {},
   "source": [
    "### 2.3 创建模型类"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91ba53a4",
   "metadata": {},
   "source": [
    "在确保隐藏层维度都正确后，将所有的隐藏层封装到一个模型类中，其中模型结构在 `__init__()` 中定义，具体的计算过程在 `forward()` 中实现。此时需要加入激活函数。在本模型中，**请在 `conv1`、`conv2` 和 `fc1` 后加入 ReLU 激活函数，并在 `fc2` 后加入 Softmax 激活函数**。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "415791e4",
   "metadata": {},
   "source": [
    "```py\n",
    "class MyModel(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.conv1 = ...\n",
    "        self.pool1 = ...\n",
    "        self.conv2 = ...\n",
    "        self.pool2 = ...\n",
    "        self.fc1 = ...\n",
    "        self.fc2 = ...\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = torch.relu(x)\n",
    "        x = self.pool1(x)\n",
    "        ...\n",
    "        return x\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "id": "9f6b79df",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-07T09:38:48.430611Z",
     "start_time": "2025-05-07T09:38:48.425618Z"
    }
   },
   "source": [
    "import torch.nn.functional as F\n",
    "class MyModel(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.conv1 = nn.Conv2d(in_channels=1, out_channels=20, kernel_size=5, stride=1)\n",
    "        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)\n",
    "        self.conv2 = nn.Conv2d(in_channels=20, out_channels=50, kernel_size=5, stride=1)\n",
    "        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)\n",
    "        self.fc1 = nn.Linear(50 * 4 * 4, 500)\n",
    "        self.fc2 = nn.Linear(500, 10)\n",
    "        self.softmax = nn.Softmax(dim=1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.pool1(x)\n",
    "\n",
    "        x = self.conv2(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.pool2(x)\n",
    "\n",
    "        x = x.view(-1, 50 * 4 * 4)\n",
    "        x = self.fc1(x)\n",
    "        x = F.relu(x)\n",
    "\n",
    "        x = self.fc2(x)\n",
    "        x = self.softmax(x)\n",
    "\n",
    "        return x"
   ],
   "outputs": [],
   "execution_count": 10
  },
  {
   "cell_type": "markdown",
   "id": "49894cba",
   "metadata": {},
   "source": [
    "再次测试输入输出的维度是否正确。如果模型编写正确，输出的维度应该是 `[6, 10]`，且输出结果为0到1之间的概率值。"
   ]
  },
  {
   "cell_type": "code",
   "id": "95558fc5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-07T09:38:54.359575Z",
     "start_time": "2025-05-07T09:38:54.333615Z"
    }
   },
   "source": [
    "np.random.seed(123)\n",
    "torch.manual_seed(123)\n",
    "\n",
    "model = MyModel()\n",
    "pred = model(smallx)\n",
    "print(pred.shape)\n",
    "print()\n",
    "print(pred)\n",
    "print()\n",
    "print(torch.sum(pred, dim=1))"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([6, 10])\n",
      "\n",
      "tensor([[0.0975, 0.1003, 0.0987, 0.1080, 0.0927, 0.0913, 0.0983, 0.1060, 0.1013,\n",
      "         0.1058],\n",
      "        [0.0971, 0.1000, 0.1006, 0.1078, 0.0939, 0.0914, 0.1014, 0.1017, 0.1025,\n",
      "         0.1037],\n",
      "        [0.0958, 0.0996, 0.0984, 0.1092, 0.0932, 0.0923, 0.0984, 0.1066, 0.1022,\n",
      "         0.1043],\n",
      "        [0.0991, 0.1006, 0.0990, 0.1091, 0.0918, 0.0889, 0.0989, 0.1061, 0.1042,\n",
      "         0.1021],\n",
      "        [0.0954, 0.1004, 0.1015, 0.1071, 0.0943, 0.0922, 0.1017, 0.1023, 0.1027,\n",
      "         0.1024],\n",
      "        [0.0962, 0.1011, 0.0987, 0.1094, 0.0923, 0.0906, 0.0958, 0.1088, 0.1024,\n",
      "         0.1048]], grad_fn=<SoftmaxBackward0>)\n",
      "\n",
      "tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000],\n",
      "       grad_fn=<SumBackward1>)\n"
     ]
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "markdown",
   "id": "3b8236f0",
   "metadata": {},
   "source": [
    "`pred` 的每一行加总为1，其中每一个元素代表对应类别的预测概率。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ca3a05a",
   "metadata": {},
   "source": [
    "我们还可以直接打印模型对象，观察隐藏层的结构："
   ]
  },
  {
   "cell_type": "code",
   "id": "20a4eb6f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-07T09:39:20.805994Z",
     "start_time": "2025-05-07T09:39:20.802473Z"
    }
   },
   "source": [
    "print(model)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MyModel(\n",
      "  (conv1): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (conv2): Conv2d(20, 50, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (fc1): Linear(in_features=800, out_features=500, bias=True)\n",
      "  (fc2): Linear(in_features=500, out_features=10, bias=True)\n",
      "  (softmax): Softmax(dim=1)\n",
      ")\n"
     ]
    }
   ],
   "execution_count": 12
  },
  {
   "cell_type": "markdown",
   "id": "0d0bc12f",
   "metadata": {},
   "source": [
    "### 2.4 定义损失函数"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffd11e33",
   "metadata": {},
   "source": [
    "对于分类问题，损失函数通常选取为负对数似然函数。在 PyTorch 中，可以使用 `torch.nn.NLLLoss` 来完成计算。其用法是先定义一个损失函数对象，然后在预测值和真实标签上调用该函数对象。注意：损失函数对象的第一个参数是预测概率的**对数值**，第二个参数是真实的标签。[文档说明](https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html)。"
   ]
  },
  {
   "cell_type": "code",
   "id": "6f4ee18e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-07T09:39:34.844942Z",
     "start_time": "2025-05-07T09:39:34.830910Z"
    }
   },
   "source": [
    "lossfn = torch.nn.NLLLoss()\n",
    "lossfn(torch.log(pred), smally)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(2.3102, grad_fn=<NllLossBackward0>)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 13
  },
  {
   "cell_type": "markdown",
   "id": "0cf163e4",
   "metadata": {},
   "source": [
    "### 2.5 编写训练循环"
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "",
   "id": "79d2b4df1064aa5b"
  },
  {
   "cell_type": "markdown",
   "id": "c53f5d1a",
   "metadata": {},
   "source": [
    "利用课上介绍的循环模板和代码示例，对模型进行迭代训练。对于本数据，选取 mini-batch 大小为200，共遍历数据3遍，优化器选为 SGD，学习率为0.001。记录每个 mini-batch 下的损失函数值存放到列表 `losses_sgd` 中，然后画出损失函数的曲线。"
   ]
  },
  {
   "cell_type": "code",
   "id": "b99d1bb8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-07T09:41:49.054748Z",
     "start_time": "2025-05-07T09:41:31.459838Z"
    }
   },
   "source": [
    "\n",
    "\n",
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "nepoch = 3\n",
    "batch_size = 200\n",
    "lr = 0.001\n",
    "\n",
    "np.random.seed(123)\n",
    "torch.manual_seed(123)\n",
    "\n",
    "model = MyModel()\n",
    "loss_fn = torch.nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=lr)\n",
    "losses_sgd = []\n",
    "\n",
    "train_dataset = torch.utils.data.TensorDataset(x, y)\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "\n",
    "for epoch in range(nepoch):\n",
    "    for batch_x, batch_y in train_loader:\n",
    "      \n",
    "        pred = model(batch_x)\n",
    "        loss = loss_fn(pred, batch_y)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        losses_sgd.append(loss.item())\n",
    "\n",
    "    print(f\"Epoch {epoch + 1}/{nepoch} ，Loss: {loss.item():.4f}\")\n",
    "\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.plot(losses_sgd)\n",
    "plt.title(\"SGD Training Loss Curve\")\n",
    "plt.xlabel(\"Iteration\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3 ，Loss: 2.3027\n",
      "Epoch 2/3 ，Loss: 2.3018\n",
      "Epoch 3/3 ，Loss: 2.3024\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ],
      "image/png": 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xiSeeEF4/ZswYp2/fvk5xcbFzyimnOP/85z+d3//+9x7mRBm743vvvcdcR+p7/fXX3WMydke6nbJ6HMdxPvroI2fw4MFOUVGR07t3b2fMmDHOz372M6djx46KnsjUTfqY/49u0yuvvOIMGTLEKSoqcjp06OBcf/31zurVq93ze/fude68805nwIABTps2bZy2bds6Q4YMcZ566iknmUw6jpNmQrzxxhudPn36OMXFxU7nzp2diy++2Pnkk0982+k4aXbNN99807nsssucTp06OZFIxDn22GOdq6++2nnnnXecVCrlXrthwwbnyiuvdNq3b+8ce+yxzv333+98+umnQnbHU089VVnv9773PQeAc/7550uvee2115xzzjnHadOmjVNSUuL069fPuf32251FixZpPZuBgYFBY8BynDxScBkYGBgYGBhIkUgkcPrpp+P444/H5MmTG7s5BgYGBgZNFMbd0cDAwMDAoED40Y9+hOHDh6NHjx4oKyvDiy++iLVr1/qyJhoYGBgYtGwYJc3AwMDAwKBAqKysxK9+9Svs378f0WgUZ5xxBiZOnOgb52VgYGBg0LJh3B0NDAwMDAwMDAwMDAyaEAwFv4GBgYGBgYGBgYGBQROCUdIMDAwMDAwMDAwMDAyaEIySZmBgYGBgYGBgYGBg0IRgiEMKCNu2sWfPHrRr1w6WZTV2cwwMDAwMDAwMDAwMGgmO46CyshLHHXccQiG1rcwoaQXEnj170KtXr8ZuhoGBgYGBgYGBgYFBE8HOnTvRs2dP5TVGSSsg2rVrByD9Itq3b9+obUkkEpg8eTKuvPJKRKPRRm2LQcuAGXMGDQ0z5gwaEma8GTQ0zJhr/qioqECvXr1cHUEFo6QVEMTFsX379k1CSWvdujXat29vPmyDBoEZcwYNDTPmDBoSZrwZNDTMmDt6oBMGZYhDDAwMDAwMDAwMDAwMmhCMkmZgYGBgYGBgYGBgYNCEYJQ0AwMDAwMDAwMDAwODJgSjpBkYGBgYGBgYGBgYGDQhGCXNwMDAwMDAwMDAwMCgCcEoaQYGBgYGBgYGBgYGBk0IRkkzMDAwMDAwMDAwMDBoQjBKmoGBgYGBgYGBgYGBQROCUdIMDAwMDAwMDAwMDAyaEIySZmBgYGBgYHDU4VB1HOvKKhq7GQYGBgZZwShpBgYGBgYGBkcdzvjjFHzj6VlYs8coagYGBs0PRkkzMDAwMDAwOGoxZ/OBxm6CgYGBQWAYJc3AwMDAwMDgqIVlWY3dBAMDA4PAMEqagYGBgYGBwVELo6IZGBg0RxglzcDAwMDAwOCohTGkGRgYNEcYJc3AwMDAwMDAwMDAwKAJwShpBgYGBgYGBkctjCHNwMCgOcIoaQYGBgYGBgZHLQxxiIGBQXOEUdIMDAwMDAwMjloYHc3AwKA5wihpBgYGBgYGBkctjI5mYGDQHGGUNAMDAwMDAwMDAwMDgyYEo6QZGBgYGBg0A/xz5hb8+M1FSKTsxm5K84LxdzQwMGiGMEqagYGBgYFBM8BjE9di6tq9+O+KPY3dlGYFo6IZGBg0RzSqkjZ69GicddZZaNeuHbp27YobbrgB69evV94ze/ZsnH/++ejcuTNKSkowYMAAPPXUU57rPvjgAwwcOBDFxcUYOHAgPvroI+b8Cy+8gCFDhqB9+/Zo3749hg0bhs8++4y55s4774RlWcx/5557bu4PbmBgYGBgkCVq48aSFgTGkGZgYNAc0ahK2owZM3Dvvfdi3rx5mDJlCpLJJK688kpUV1dL72nTpg3uu+8+zJw5E2vXrsXDDz+Mhx9+GC+//LJ7zdy5c3HLLbfgtttuw/Lly3HbbbfhO9/5DubPn+9e07NnT4wZMwaLFi3CokWLcNlll+H666/H6tWrmfq+8Y1voLS01P1v4sSJ+e8IAwMDAwMDg4LAMrY0AwODZohIY1Y+adIk5u/XX38dXbt2xeLFi3HRRRcJ7xk6dCiGDh3q/t23b198+OGHmDVrFu666y4AwNNPP43hw4dj5MiRAICRI0dixowZePrpp/Huu+8CAK699lqm3MceewwvvPAC5s2bh1NPPdU9XlxcjO7du+f+sAYGBgYGBgYGBgYGBhpoVCWNx5EjRwAAnTp10r5n6dKlmDNnDv70pz+5x+bOnYtf/OIXzHVXXXUVnn76aWEZqVQK7733HqqrqzFs2DDm3JdffomuXbvimGOOwcUXX4zHHnsMXbt2FZYTi8UQi8XcvysqKgAAiUQCiURC+5kKAVJ/Y7fDoOXAjDmDhkZLGXOpVOqof8Zc4TiO+9u2C9NfLWW8GTQdmDHX/BHk3TUZJc1xHDz44IO44IILMGjQIN/re/bsif379yOZTGLUqFH48Y9/7J4rKytDt27dmOu7deuGsrIy5tjKlSsxbNgw1NXVoW3btvjoo48wcOBA9/zVV1+Nb3/72+jTpw+2bt2KRx55BJdddhkWL16M4uJiT5tGjx6NRx991HN88uTJaN26te8zNQSmTJnS2E0waGEwY86goXH0jrn0kr1q1UpM3L+ikdvStGE7AOmvlStXot2+wvXX0TveDJoqzJhrvqipqdG+tskoaffddx9WrFiB2bNna10/a9YsVFVVYd68efjNb36D/v3749Zbb3XPW1yksOM4nmMnn3wyli1bhvLycnzwwQe44447MGPGDFdRu+WWW9xrBw0ahDPPPBN9+vTBp59+iptuusnTppEjR+LBBx90/66oqECvXr1w5ZVXon379lrPVSgkEglMmTIFw4cPRzQabdS2GLQMmDFn0NA42sfcA3MnAwAGDx6MEWf2bOTWNG0kUzZ+MW8qAGBIgfrraB9vBk0PZsw1fxAvOx00CSXt/vvvxyeffIKZM2eiZ0+9ifSEE04AkF6s9u7di1GjRrlKWvfu3T1Ws3379nmsa0VFRejfvz8A4Mwzz8TChQvxzDPP4KWXXhLW2aNHD/Tp0wcbN24Uni8uLhZa2KLRaJP5mJpSWwxaBsyYM2hoHO1jLhwOH9XPlw/YVsr9HYkUtr+O9vFm0PRgxlzzRZD31qjsjo7j4L777sOHH36IadOmuYpXNuXQsWDDhg3zmIInT56M8847L1A5PA4ePIidO3eiR48eWbWzsfHq+hC+/9pC2Lbjf3ETRMp2MGvjflTUGV9sAwMDAwM5qJA0w+5oYGDQLNGolrR7770X77zzDiZMmIB27dq51q8OHTqgpKQEQNqFcPfu3Rg7diwA4Pnnn0fv3r0xYMAAAOm8aU8++STuv/9+t9wHHngAF110Ef7yl7/g+uuvx4QJEzB16lTGlfKhhx7C1VdfjV69eqGyshLjxo3Dl19+6TJOVlVVYdSoUfjWt76FHj16YNu2bXjooYfQpUsX3HjjjQ3SP/lEynaw4lAIOHQYWw5UoX/Xdo3dpMD456wtGPPZOgw+vgP+c/8Fjd0cAwMDg0aBUTn8YbNamoGBgUGzQ6MqaS+88AIA4JJLLmGOv/7667jzzjsBAKWlpdixY4d7zrZtjBw5Elu3bkUkEkG/fv0wZswY3H333e415513HsaNG4eHH34YjzzyCPr164fx48fjnHPOca/Zu3cvbrvtNpSWlqJDhw4YMmQIJk2ahOHDhwNIu5OsXLkSY8eORXl5OXr06IFLL70U48ePR7t2zU/BoZmumis+WLwLALBy95FGbomBgYGBQVNGM3UYMTAwMHDRqEqajuLwxhtvMH/ff//9jNVMhptvvhk333yz9Pyrr76qvL+kpASff/65bz3NBeyCZbYVDQwMDAyOXtCWNLPiGRgYNEc0akyaQcPhaLCkGRgYGBgY6MCxM795ZmcDAwOD5gCjpLUQHA2uH2adNTAwMDBzoQ5SxpJmYGDQzGGUtBYCxvXDrFgGBgYGBkcxbOM9YmBg0MxhlLQWgqPBkmZgYGBgYKADWkkzy5+BgUFzhFHSWgzMMmVgYGBg0DJAG9JMTLaBgUFzhFHSWgiOBkuaSUiaHWZs2I/Rn61FMmX7X2xgYNDkUci5cM2eCrw1dxvsZr5oGEuagYFBc0ejUvAbNBwMHXHLxR2vLQAAnNC5Db57du9Gbo2BgUFTxohnZwEASooiuPnrPRu5NdnDNpY0AwqPfboG+ypjePqW0w3bp0GzgbGktRA0801Rgzxgd3ltwetIpGx8vroMB6tiBa/LwMCgcFizp6Kxm5ATaEug0dFaNhzHwT9nbcWEZXuwaV9VYzfHwEAbRklrITA7iQYNMQRenrkFd7+1GDf+Y07hKzMwMDCQgPYeMZuULRuJlHF9NWieMEpaCwG9SJkFKxgWbTuEu99ahJ2Hahq7KU0en60qBQDsMH1lYFA4GG8tXzDujkY0b9GIU/HYkZD5eAyaD0xMWgsBmzOmeS5YjeVGfvOLcwEAB6vieP+n5zVOI/IAI6gYGBi0FBhLmgFBPJlR0qJhY5swaD4wo7WFwDGWtJyx87CxDhkYGBg0BzgsB3/jNcSg0ZEwzMYGzRRGSWshoBcss15lB5MCwMDAoLHQ0HHFzZ0Aj5bLzZLXskFb0oz8Y9CcYJS0FgI2Js3MUtmguQstBgYGzRf0tG2mIn8w7o7GfaRFI0YraUZlN2hGMEpaC4FtLGk5o7kLRg3x3o210cCgMDDTdjCYZNYGBLQlzejrBs0JRklrIXCMJc3gKMaKXeV4YNzSBskFZ2DQGDBpVILBxGEbENDsjuY7MmhOMOyOLQRGMcsdlvF3bLK47rmvAKSp/z+65/xGbo2BQf7R0DN4c5/tWO8Rs/61ZNDEIUZhN2hOMJa0FgITk2bQEt76xr1Vjd0EA4OCgJ63zYaRP4wwbkDAEoeYgWHQfGCUtBYCE5Nm0BJgqJYNjlYcDfP2s19sxKuztzZIXSlKSzMbky0bjJLWiO0wMAgK4+7YUmAsaQwq6hJoVxwxO9JHGVJm+9zAoEliT3kt/jZlAwDgzvP6Ihwq7Nxr0s4YEMQY4hAzGAyaD4wlrYXgaGC6ypdC9dWmAxgyajJ+N2F1wPrzUr1BAZE0SprBUYrmLlzWJVLu74Z4FtsQhxjUg4lJM84WBs0IRklrIaAXqcbyyR7z2TqMeGYWauLJRqmf4MnJ6wEAb83b3qjtaGg0cxnPwKBFo7nnSaM32RrC4s1uTJrJryUjbvKkGTRTGCWthaApxKS9OGMz1pRW4KOluxunAfXI9vmNJc3AwKCx0ODsjnme72jvxoaxpDX+mmfQNMBS8DdiQ5oYHvl4FX713vLGboaBAkZJayFoSjljGjtuKNvaTaJmAwODxkJzZ6Wj58+GWAKcJuA9YtA0EDcxaR7UJVJ4a952vL94l8kv2oRhlLQWAnpiaq6TlFGRmj6MtdHAoDCgFZvm+J3RbW6IjTq6jma65BnkCSwFfyM2pAmBlgNTKdMpTRVGSWshMK4fFLLsgOYoGNFoCF/8Fj+2DAwKhaPo27IbOCatsb1HDBoX8ZSxpBk0TxglrYWAnpZauutH9u6OBgYGBo2D5k54wFjSGmANYtwdm3nfGeQG1t2xERvShOA0c8t8S4FR0loI2AXLIBu0hJxqjuPgrrGL8Mt/ZxdM3AK6yMCgUdDc99bYuGhjSTNoONCWNCMBpcFu3DdaMwx8YJS0FoKmFJPW2HJ8i52QNJ57x6EaTF6zFx8s2cXkljEwaIo4XB1vMeOUnreb42YIo6Q1wCuzjRRqUA9jSfOipXtUNRcYJa2FoCnFpGXtbpgnwSRb15dmKBcJUV4Tx7YD1cJzCRNAbNBMsPNQDYb+cQqu/fvsxm5Kg6C56xwMUUEDPABDHFLw2gyaMthk1mY0AM2fiKilwChpLQT5dDVxHAcvz9yMGRv259iqxkHWj3+UTGSn/2EKLnnyS+w8VOM55zQhi6uBgQqfriwFAKwrq2zkljQMWEr5xmtHtqCb3BCCspnLDAjYZNYGAPt9mM+j6cIoaS0EjCUtx7JmbTyAP09chzteW5BjSQaNieW7yj3H7GYqCIaOEgW6uSCZsnHLS3PxuwmrGq0NzWl85gO0B0BDPHq+Y3Ab2uW+uc5lBvmHyZPmhemG5gGjpLUQsAtWbl/nzsNeC0wQNLY8ne3jN3a7cwX/2GGBENaU3GKDIBI2U1lDYu6Wg5i/9RDGzt3eaG1oaYx9zT05M93khsiTZohDDAhiKZMnjYdRVpsHjGTTQpBPV5mc78/tdoM8ISwwP+W6291YimzEmNIaFMlmJvXuLq/F7vLaxm5GTmhoht58f1EN7X7Ieo80r/FqkF+YZNZeMDGu5vtosog0dgMMGgZOHncVG+tzNsGt+UUk7O3QXGMXG2tsiBROg8KhOfV2PGnj/DHTAADr//QNFEfCjdyi7MAIUs1QpqKb3BCEnA4rhRq0YCRMMmsPmqvXTEuDsaS1ELAfZO7EIbmgsQW87NklG7vluYN2MwqHvJ9/c3URMpa0lgfdaaiyLuH+PlKbUFzZtNHckzM3dEwaPdcZwbxlw8SkedHQeQsNsoNR0loIbEf8Oxs0d3fHbJXMhlQDdh2uyTsDmuM4zI6iOCaNviGv1RcUJibNQIYQPc6b0Zjm0dx3vhszJq059pdB/mDYHb1oaPdpg+xgJJsWAjuPvh+NFbRuNbINrqEMaROW7cYFf5mOX763PK/lOg4bS9TcY9JiyZT721jSGha0VTnX+WDz/iqUHgkeL6ZbL/3dNifrMI/mLlQ1tCXNyePGpEHzRiqPnkRHC/LpXWVQOBglrYUgnwtWrvcbcVqNZ7/YCAD4aOnuvJedoHYUxTFpzcNFaMKy3Tj54Ul4f/EuAC0zJq30SC0e+XgVNu1r+DxhdG/nMh8crIrh8r/OwLDR03Jukwz05k5zdBM8WtColjTz3ls0GE+iBoiHbA6gvwizidF0YZS0FoJ8un409+85ewr+5q8IJKgVSvQ0dEB/U564Hxi3DADwq3prY0u0pN3zryV4a9523Pj8nEZtRy7K/NYD1Vnfq1stLaA35THth2afzLqBY2BMnjQDAjp0wAyFNJg+MZ3SZGGUtBaCfC6Q2ZjGjwZzekO5Oxayp5Ip9WKVtGnf/ebzzmhLmmys5TvGr7GxctcRAEBlLNmo7cjFKkK/t6DvR/fqfOaIbEw0d8tQQ5MSGRc3A4KGdrVtDjDEIc0DRklrIWAX+NyQzffclOYAlYCzavcRbMthd7+pgRdOGCVN0A2pZrq7FqWIQxIpb8Of+HwdznxsarPPlUWjMV8PG+eVHyUtEdAPSduSdpQQSDBRxQ3xHHnelKKb3BDujk4e1zyD5o2jZaMmn2A9DEyfNFUYJa2FwMnjJJXNLm5TmgJkj7+vsg7f/PtsXPLklznXMX/LQdfS0ZjgnzXuky8m2UxpqxlhX5CE6fnpm3GoOo5np25syGa1COTLkpYUKNf5wNHi9tbclQ7GmtEQMWnNdC4zyD+Olo2afOJomRePdhglrYVANyYtmbKxZk+FUpHTWV/5+/OxSBba3XDnIbWVRTdP2oGqGG55eR6ufW52PpqVE3gLKuPOKHglrGBTyJblFxEfJY3gKEh156Ixd4Tp+Mxcxomfcp0P0P2UasbSCNPyZvgcDHGIiUkzaEA01/yfhYRRXJsHjJLWQsDmSZN/kb/+YAVGPDsLz0/fJL3G74PevL8KX//TVLw0Y7NWnQ0NWUtoQZ9YB7IRhPdW1Lm/a+JZxAvlsav4olh3Rx9LWjNazWgFWuTuaFA45DJOQjm8N12LfkO72RUKzd2S5jSwoGwEcwMCk9jcC12Z0KBxYZS0FgLdXZMPl6Rp35+fvll6jZ9wNOqT1ThUHcfoz9Zp1dnQkCleNCU9SX5JX6prhKEFz4G/+xxvz9seuI35Av+otLVC1AvNVYilW10oi0xTQ2O+KXoOyMUqQt8a9L3pVnu0kAY0e3ZH6ndDbACxfdQMO8wgb2juOQYLgeaSbqelwyhpLQRBd01Ueaf8bq8SsM01hzkgEsp8DiR2ixZAdV3l+Ose/nhVoHbks6t4hZq2VhQkJq2B/An58UkvOC1FSWtMsHmHsh+x9BgLGpNGX62yeDd0fq5CQfd5myrocdIQ7yGluTFp0HywePshvDN/R+DxbxI3e+FIfhs0LRglrYUgaJ40tZKmLqA2nlLW31RBEQS6lrRshIlQEwp84nffk6mjIybNq6RlfrcUJS3bT+pITQK/m7AKS3Yczrpu+nvOxZJGlxOU3RGac9pRaUlrvGYIUVGXwOtfbcU+ytWbB+N22iAxaUfHezfI4FsvzMVDH63EV5sOBrrPuPZ5YRTX5gGjpLUQsLsmuVnS/IT3mgIpaflSfeiWVMeSuOWluXht9lZGCCKWNLrdurpXU8qrzAp2DhI+lrLmwu4Y5l4GPaZNTJoaYyatxdi523HTP7JPgp2v+KKc3B2p36qxyuQDasb6e9CNtobEbz5YgUf/swZ3vL5Qek1DC4XN3T3UQI4tB6oCXd+Uv53GAj0XNuUN2ZYOo6S1EAQVqlSKhh+dv1hJ86+zwUC15a152zF/6yH84b9rmDYmkkRJC168LgtkQ4BXyJO+MWlqS1tTAT8+6QWnpVjSssWmfcEEHBHoLrZtB5v3V2FtaUXgcnJxd2TLkZ8rNKvgvC0HcemTX+KLtXtRUZfIe/kETdmSNnFlGQCoxwDjdgr8c+YWjPxwRcEUtubiFWAQHEFdrM1Y8ILJk2Y6pcnCKGktBEHpiJXujlBPeHUJr5LW5KSKetRQ8XP0c7kxadQDWpq2vKajoomIQ9S72bQlTSY8bdhbieuem41p6/bmp5FZwOPuSP1WKWlNWfFsKORjE4FxU0zZuPyvM3D1M7NwpDaYkkLPH/EclGuVJY1xzSyAMPLdl+dh64Fq/OjNRRgyajIq86io7TxUg0mryuA4DjM//fG/azB9/b681SOC7nynC7rrU46DxyauxbsLdmLhtuzdbnXryya3p0HTRdC3adwdvWjKmz4GGRglrYUgqH8+cSdzHAdHalihg1X4vGXVCpQ0ps5shcQ8WaiYFlNl0tYYEpOWzQ5TU5rw+HedyENM2v3vLMWKXUfwwzcW5aWN2UBNHNKU3kDTQz6+Ill/H6iKBSonJ+IQTcFL1y0yX1i5O39J7C98fDr+9+3F9Yoae+4HCtfCfCDfDgGyZNai9SLf9TWpSdkgZwRdls1Y8EJ3/jRoXBglrYXADjhHheqF4D/8dw1O+8NkfEnv2vq4Top2q9lJMvcJIRcXGfpeWg6h25hLTFquj5dP9x+6JMdhk1mL3p1OTFpQQbwQUBGHmAVHjXwQ26Qk7qVBS3YYJS1oTJqeC1OhLWk8CjH8Fmw71CBW4ELGismU5UJ5HhjikKMXwdkd6d9mLAAmTq+5wChpLQRsnjQNS1q9EPz6V9sAAGPonGd0uZrbUkGVxCDl5QJaXmWIQ5JeCn7diaypMCUdro4jlmAFXz93x5Tt/7xNYZHjFY2mvlO6aV8Vnvx8vccq3RgI5WHWZyxgtEtwQAUwF3dHbUsafV0DKGmF+D4sWA3ispdr01tF5YNLpixnu2mw81ANHhi3FKsklktGCM2qBoOmiqDfWEMnUm8OMEpa84BR0loIAsekcQsnLUCJPu5N+ypxx2sLpLTetICRjwkhF0GIvpOOu+DjbACeASm4QpoN8jFf7q2ow9A/TsEVf5vBHKddyiatLsNejjJbx5LWFOZzVUwa/Xvj3kqUHqktSBu2HqjGE5+vw6HquO+1Vz41A89N34Q/fbqmIG0JgnzEGrFuijnEktl0OTl804omNLSAVghrnWU1jCCVaxVtiyNahbNKWnZ13f3WYkxYtgff/Pts4XnWepJdHQZNE0G/BVZhN4MBaHg3cIPsoJhRDY4mqLLLv/HVVrz21Tb868fnuMd4IVgWy0TKuvP1hdh1uBYzNuyX1O+9Rxe27SAUYkXLXOYUWXgc4+6Y9Lo76i70TWHCm7E+/R54Igf6PX64ZDcmrSrDmj98wz2mY0lrAo+ntKSR33sr6jD8qZnMdfmMs7n277NRFUtiw94q/PP2M5XXkm7NZ7xStshHH4hcg4HgrmsMo2oOFPwq1kaesKLQKEQVIath5pVcvQDaKJQ00SYYgKz9Hf1YSk0eqKMXQZXulB18HT/aoZIJDZoOGtWSNnr0aJx11llo164dunbtihtuuAHr169X3jN79mycf/756Ny5M0pKSjBgwAA89dRTnus++OADDBw4EMXFxRg4cCA++ugj5vwLL7yAIUOGoH379mjfvj2GDRuGzz77jLnGcRyMGjUKxx13HEpKSnDJJZdg9erVuT94I0BlSRv1nzXYcaiG2eX3KGlJ8SRHytp12GutiITEVqogk+Tnq8sw5NHJmLKGZRLM16TCxqRlficE7I66C31TmO9iSXEwPi8I8+kSUhqWNFXfNxSzZSTM1SQYkxv2Vha0DVX1zKBL663Hi7cfxsMfr1S6NJYUhQvaJh3kIyaNtjDTFrCgRTP57QJKT/rEIdSYbgAJrTCWNKtB9v9zraNNkVxJY9zJqTGTj/HoV18TmJIN8oig1jBmemgKC3QDoi6RcjedaZjvo3mgUZW0GTNm4N5778W8efMwZcoUJJNJXHnllaiurpbe06ZNG9x3332YOXMm1q5di4cffhgPP/wwXn75ZfeauXPn4pZbbsFtt92G5cuX47bbbsN3vvMdzJ8/372mZ8+eGDNmDBYtWoRFixbhsssuw/XXX88oYY8//jj+9re/4bnnnsPChQvRvXt3DB8+HJWVhRX+CgGdXZOkYuFkLGnwLwtgBVI/RkgZ7n5rMapiSfxkbP6YBOn2szFpmeMxoSVN192x8ae8mGBSBlh3RgJ6AtdKZq14vIZ6cm8ya+/vfFOIy5Gu51svzMHb83Zg9GdrpVe2bhJKWu5lsDFptCUtWOH0EEtIxqxOG5QU/FSxDUEc4vf9b9hbibFztwVyE7XQQO6OOdahcneUWdKyHo4+N2azwWbQPJCLu2NLsqTFkzZO/8NknDv6C883kK1MZtCwaFR3x0mTJjF/v/766+jatSsWL16Miy66SHjP0KFDMXToUPfvvn374sMPP8SsWbNw1113AQCefvppDB8+HCNHjgQAjBw5EjNmzMDTTz+Nd999FwBw7bXXMuU+9thjeOGFFzBv3jyceuqpcBwHTz/9NH7729/ipptuAgC8+eab6NatG9555x3cfffd+emEBkLQiYm3pDE7MZq+/tFwZg+AJS4J1hYRcopJY9wdM89JKygucUgWbhK5szvmdj8gVtIcxxEKhrvLa3FClzYAuGTWsvbl3rycEQrJ3R3JWBNt0BfKFY3G5v1yN6ySaON7mOc7T1o8mX2nypQ9Hei45gLspkyDuDv6nL+y3gXXAvC9c/ooc1K6sHRKzh3ZxOvQVvvWxfJNCLpkej3hv+V8wRAjHL0InMxa0+p+tGFPeS3qEjbqEnHEUzaKI/TGOe1h0BitM9BB40sMFI4cScdrdOrUSfuepUuXYs6cOfjTn/7kHps7dy5+8YtfMNddddVVePrpp4VlpFIpvPfee6iursawYcMAAFu3bkVZWRmuvPJK97ri4mJcfPHFmDNnjlBJi8ViiMUy9OQVFRUAgEQigUSicVndkslM0uZEMiVsj01F31uWw1wTT9nu38lUZlFOJBJISEZRys7cE6fKSqSS2v3RuijsuuTRil48nkDUynKypScn6lnq4gnqd7qN8USm32zqeVSIC64J8v6ZHFTUfevKKrG/KoYL+3fxLaM25q0vZduoiyc9xzfvPYKeHYrS9SXpdyt+T7akffWN95wrxNinXQASiQRLQJFMtzuV8j6r7ei9wyCwwD6jbTvSOlpFrIL0R6AyVe9Pt76k+LuJB5zrEtT3Rb45XdDzUCyeQCIRFo45uo64ZEznE7p1vDlnG8Z8tg4PDj8Rt5/bW3mtY9vMXESQ72dJUDnL7JTet1JOEee0ioSk9/DvmiCVzP2diO6nN6SSms+Sbb2Nvb63NCRSYhlGBpboKNi9TQ1BxpxtZ76z2ro4QpSlm5UJCz8vGmQQpK+bjJLmOA4efPBBXHDBBRg0aJDv9T179sT+/fuRTCYxatQo/PjHP3bPlZWVoVu3bsz13bp1Q1lZGXNs5cqVGDZsGOrq6tC2bVt89NFHGDhwoFsGuY8vZ/v27cI2jR49Go8++qjn+OTJk9G6dWvfZyok1u+2AKR3UdauXYuJFTTLXHoY7N+3D0T8rSg/gokTJ7rn4slU/d/A5m0h97rJk6egTTRTBo26eMK950Bd5pp1a9dhYoXcJYxGFGEQv5by8nL39+efT0arLEdvTW2mzPUb1oP0y5x5C9zfy1asRPv9K1Bak2l3ZVW1+zwAkLSB2XstnHKMg24lmfK3V2buIaDv821fTaZ99H0PzE2X+dvTk+haAhyJp3cIOxZ7y1i7PfOOCLZu246SCDzHP5u1CNWb0ovYRurdfjVnDspWectOJsXtA4Dy8sy5KVOmMP/mE9VVbBuqqT5bsDD9PBuPZMY8wc6dOzFxovj7DY70+4jF6phv5dDhw0y/pLMgpM8d3LsHEyfuymv9QLDxtX9f5h0HuY/Gir2Zvl20ZKn7e/qXX6Jrifw+HmsOZ8pZvnIVjjmwUvveLVszz/HFtGnoRH0H9JjbUQWQvlq8eAns7fneSWe/9cVL/OpIX79pf9qt/4+frkOXQ4IPjbp2y5YtaHVok6eubN+f44gtzen9sHQdmzdvxsSJG33Louf2vWWlmDhxt/C6ZQcz73r9xk0g727evLnYm0Wot5PyzkNLDlhYcsDC9/vb2LYjMz7Kysqy7isdFGKOMxAhPc42bNiAibVq/gIaSWqsrF69BhMPN09uARo6Y+5QDCB9NnHS5HpZLY0N1Pq4aPFixLe2HAtjY6Ompkb72iajpN13331YsWIFZs8W0+nymDVrFqqqqjBv3jz85je/Qf/+/XHrrbe653mXHsdxPMdOPvlkLFu2DOXl5fjggw9wxx13YMaMGa6iplsOwciRI/Hggw+6f1dUVKBXr1648sor0b59e63nKhS2TNsI7NgKADjp5AEYcdEJ7rkH5k4GAHTt2g04nGYF7NK5I0aMONs958DCiBEjAADLPluPL0vTgu7lV1yBTm2K3OtohEJhjBhxFQBg28FqYOlXwvpVeG7zVziyLy3MHHPMMdhelba2XjF8ONqXRFW3SvH42plALE09P+DkAfjvjrQgMuT0M4B1ywEA/U8+BSMu6It1ZZXA8rkAgJLWrTFixIVuOc98sQkfbduCjwBs/GPG4rp0ZzmwagFTJ+k7HTy5bhYOxmo995E+7jPoLFx0YhcM+H16kl7+yGVozQXsL/50HbBnB3Osd+/e6Zio3ayScmzv/hgx/EQAwJKJ64DS9H3nnHsuzu7rtWr/36Kprn8E/1yv7ZzvvqPhw4djypQpGD58OKLR7N6VDC9vn4vdNZVuG+h3+vUzz8RlJx+LeVsO4bk1bCxjr169MGLEqXlpA3kfJSUlGDHiIvfvjh3T3w7B/soYMD+dCqH/CX0wYsQpea0f0BtfZO76b/kyrDy8T/s+EQ7P3wFsSedOHDhoCLApLfRceNHF6HdsG+W9hK0VAErW7wfWLQUAnDTgFIw4v692G+b/Zw1QllZ4L77kEvTq2BqJRMIz5lbsOoK/rkzHI592+ukYMaSH/oNqgJ/7Tj99KEYM7q59PSB/D+TaE/v1wzkndgZWs+M5m/f38bI9ePzzDXjxf4ZiSM8OzLmaeBL/b8E0AED//v3ceUGFNaUVwNJ5AIDu3XtgxIjThNdZq8rw+oYVAICeffq689O55w7D1/t0DPwcv144Nb1Thkw/PPBIur+2tOqHXn0SwN70+OjarRtGjBgqLigHiMabQeFAvof+/U/EiMv7a9/34PwprgfBgFOCzTNNDUHG3J7yWjy6ZBYA4OLLLkfXdpmdrGM2H8TzaxYDAIYOPQNXndpNWIZB/kG87HTQJJS0+++/H5988glmzpyJnj17at1zwglpIX/w4MHYu3cvRo0a5Spp3bt391jN9u3b57GKFRUVoX//9Id+5plnYuHChXjmmWfw0ksvoXv39CJbVlaGHj16KMshKC4uRnGx16wRjUYbfQIPhTIWhVA4JGwPrXxGBNe4f1PXhSMR6bOlbOBn41fgSG0Cf7whYx21QuL6RWhfUgSguv4ZqPZFculTupxMv6TonGmwEI1GEQpnzjsAU+eC7eXub/p4OOyNy+Db6jgOdhyqQa+OrT0xGfR7ED1jNBIBqPd5pM5BhzbsdQmRj7kVgi2Itncsy63Hoc6Hw+J3S/v38+dFbS/E+I9QfRyNRpmYk3AoXF+nd3oLWfpjTxchqv+AdB/Qf9ckMy7QZFzlG35lzt54AD8fvxSjbxqCMJXNOtu2WNT4c+j5IBx2y9xfGcM783fgO2f1RI8OafPan/67Bh8v24PPHrgQx7YrZuallBOsbxzKIsyPVXrM0d+wFQoXfC4OMr8R+F0fDocRDnvHczbP8v8+SFvt7hu3HHNHXs6ci9iZdxnSfI46ygvTCsnfocW868zxkGSeCQL+/opYEpaVGR/8N5lv5DrH1cZT+GrTAZzfv0uTYIDNBvGkjVv/OQ+n9zoG3z2rF1btOYIbTj8+LzGwPIJ+Y7S7o+64zgdStoNHJqzCGb074uav68m2utAZc6Fwxq3O4dY+el4MhQs/LxpkEKSvG5Xd0XEc3Hffffjwww8xbdo0V/HKphw6FmzYsGEeU/DkyZNx3nnnaZdzwgknoHv37kw58XgcM2bM8C2nKSJoELUqmN3WYQBEOo7ts1VlmLP5ILbuzzB2BmESakf5NBYi+DclIAsBKHZHOpk1p/jImOJ0YprHzt2Oi5/4Er/7RObmpAZDmiAI9hdR7gKOOBcVdbsOBT9LAtM4LhIKBv4GJzYJ+cyiFXWZhTKXhM254PuvzseBqjh+MnaRb3t1QI8T+pnosX/PvxbjqakbcMdrGavyK7O34kBVDK/O3lp/vbgcHdiapD70qabA7pgNrALkSauq88a4ZVNDXJJDU1U2PT8VZg6xmPHRENPUhGW7sXpPdnkQf/PhCvx47CL83wcr8tyqhsO0dfuwePthvDp7K4Y/NRO/GL8cn68u878xCwT5FhzH4dJ1FKBBEkxaVYZ35u/Ar95b3nCVUqDnuwQ3v6rSMhk0HTSqJe3ee+/FO++8gwkTJqBdu3au9atDhw4oKUnvvI4cORK7d+/G2LFjAQDPP/88evfujQEDBgBI50178skncf/997vlPvDAA7jooovwl7/8Bddffz0mTJiAqVOnMq6UDz30EK6++mr06tULlZWVGDduHL788kuXcdKyLPz85z/Hn//8Z5x44ok48cQT8ec//xmtW7fG9773vQbpn3yCyYkh+SLpDS9V7hqGIU2X8ZD6HWSSbNcqs+NQSSVmznZOOVwdx+7yTE43eiGnhQ03T5qC5ltGoa3DPPXE52l/+rfn7cCfbhjMnPNlWLNYJkrR6xTlSbNt8bunnytoMutY0karaMPv/Ooks24oFi+edp7v4wpq3CaaAI1WvtkdacWfPr5w22EAwIa9XrZLx31HmWOB2R01Kfjp99EQ7I4Bc3JrwbKsvO8+1AnmiGwUJpWwd++/lmB/VQzjfnIuS4gkUeyDQDWMLUtvwylfmLflEB4YtwwAsG3MNYHvn7BsDwDgk+V78Oyt+XfLbAiI+njFriP4xqD8uhcDwZQK/tqGZHc8XBP3v6iASEnmacAks24uaFQl7YUXXgAAXHLJJczx119/HXfeeScAoLS0FDt2ZGJrbNvGyJEjsXXrVkQiEfTr1w9jxoxh2BbPO+88jBs3Dg8//DAeeeQR9OvXD+PHj8c555zjXrN3717cdtttKC0tRYcOHTBkyBBMmjQJw4cPd6/59a9/jdraWtxzzz04fPgwzjnnHEyePBnt2rUrQG8UFkHzhCgtaVnsStGlLd1xGNc9NxsPXzMQZ5+gZvKkLSbllLCb7aQy9I+shZVWdug8TWIKfrZOfmeKQKdlKtZpnUdjLWleCCn44UCU45quTydPGn2cV9IKlJfWA3p8PjVlA/ZWZCzppHkNpQ/5PXMFZbFoLEsajXwkD2bym6WCL/bkOoaxNaB2w1pK5PUy81VztaQh/xZi0fyVTR2MlwbYd/LpylIAwMZ9VVwy68Ja0ixwymPea2Cxfm/zy52abxQqKTmBE1CGIeA3ZhpSH2mo9VAGZhM6yStp1HVGSWuyaFQlTWdyfuONN5i/77//fsZqJsPNN9+Mm2++WXr+1Vdf9S3DsiyMGjUKo0aN8r22qSOoaTuSB3dHGaavT5OTfOelub67jrQccYiies7XnJKSWNLIhKZaGGQ7/zp9kos1w+LqTgnaIVLSbEfcZvq5/CxpjuMw16sm/kKCVtKe+YJnoEs3QvSs2eSB8oOfcEJb0oJaiwqBfMgNtOCT1HR3o0HGEGNJC6jA0m1Q6Xd0mxrCklYIRdCymm4ya9kcqbJg0BtihdCbeffQQuvmhYi7am4Qbezms1uyTb7Mr8ctKXGz0pIG9Vpv0DTQqDFpBg0HPdM2FTSucnf0seII69e8jodOXFQuECWwBih3R8VufVKy8suUGxpB87fy96t8zQEglhC5MnknakDu7ijqe175E7lVNgRUll7XktZQ7o6K+DgAqKPehcz6Wmiwrsy5l6fj7qhzv6wcHei6s7FJWwvf/4lCKGmwGmY8U1XoCtj0voNqd15mSSuM5dHScvHPFwqUj7tZISyQJnlX8FwgcmnXgXezIP1veU2ciRcuBPL5/NlAGZNGf7cNHsltoAujpLUQ6Lh+0AsZEYJFwnAqC6FHZO3Rgaz8bOQg0UJNtysucndUWdIkArdoAeEVOpWSIS4z89uyLKZuUTtErmOO4/gqCX6WtDpO+eOVtobaUFZtIpBmi/qlEIumnyWNfhcNQVwhQtjS24DRBStwB9+RFSnSsrFZE0/i46W7caSGFah03XUamjQgVYCgtMM1cfx5ol5uyVyQjbDG9j214cO9k1wV+yDgY9IKjcawpL2/eBfGzt3W4PXKIJpX8tktrNKtf59os6AukcLpf5iCIaMmF3TjprENrLTYxW+CsZtXDdUiNZIpG//71mK8NGNzYzelycAoaS0EfKyACLQiQWjhozyNHryKk84upczq5AfZQpuNMCEqi7GkpejfxN0xc60ucYioO3iFIeiizred2SETuTsKOPhtxxH2AcOwZ6uFpzquXDGLZOGhjpn0WmnyiU37KrFsZ7n7t9+bTCSztxblC3Sah3wIlCy7Y3CBm8wZTDykpG9+N2E1fj5+GX48dqG0DapqGeKQBhDcs53reNDtfmPOtnTOxgIjm09G5uJI/+Z5T+hvItvPlN5wWbLjMDbtq6TO6RPL5AP5lMWfn77J9xrbdvCr95bjdxNWo/RIre/1DQGxklYoS1qQ+7zl7K2oc/8OGgsbBI1tYKW/Af456W5pKjFpk9fsxaTVZRj92brGbkqTQZPIk2ZQeOjQ19MCDNl5LwqHPIJ5iitLZ5LLVjiSk1cEL0skPMkCa4XEIdz9MmFM1OaEbaMEVF6SgLO3R0GUEJ4QiNwQHYjfAxOzk6MljS23cBO/0pJWX22h1t4r/jaT+dtPDqEVs8YiDmEtabmXJ7eK6N6vLofGx0t3A8iwRRJoszsy9TYnJS0vxSCRshGlfNFaRb1zOkE2/cOk5JCUZXHXxfJsSbvpH3OYcW1ZFlJZWHizRa66SMjKfBNPfL4eV53aHf27tpVeTz9OZV0SPTrkVn8+IIxJy2P5zAZAgE1afs3Ldl06WBXDe4t34aYzjkfXdq207mlsS1pKISfIvtvGRHXMmxakpcNY0loI2NxW4mtoIYlMuEUR7xDh8xPJFny27GyVNPFx0US7dMdh3PbqfKyX7Dj7uSHGUynqt9fdkb89SEwaP0GGNZQMGrzAw1gyBO0QvRPbEfdBkJg0nrZbFPtG0Fibc6TaQpB0iMadn/sgo6Q1kl8JLT/lw92RHnIJRrHXe+kZa6e4HBoRgTUf8M5DfnUBDWNJy1cd+SA52bi3EgN/NwmPT8rsTKtSZmRTo4zQwWvByPwuBHGIl9ip4SxpuX5TES55oV+sVFOxfNAotLtj0FyvmWs5Jc1zXq+ce/61BGM+W4cfvbFIu25d9/rKugTWllZol6sLlTt5tkQshUTTaEXTglHSWgh0aPMZS5rr7pgZIkkJmYZKUM+UnZ1wKnV3FBy+8R9zMGvjAdz+2nzhPSJlht1p8lrVVEyWsp1/nZi0oG4gfNPpukXtEFrSHEeipInbKep6j7ujwlxV2AlXYTkpoLujaDzy75Kvls2/1zjLEOvumDmebTwGfV8yX5Y0iVU2Ksm+nVIoX2MmrcdPxi6CbbNspA3B7pgvl9Z8KHuPf74eiZSDf3yZifEoUSlpjvi3CjI3NLr9PDtlPmLS/PKk0RsihX7tuVqng8coN5yVUBeiR2gKxCEid0fV3zLM33oIALByd4CE5ZqPf9lfZ+DqZ2Zh7uaD+mVrgGWvZmWCho7V1UJTaUcTglHSWgjoiWhNaYVQkKB3ssncQitpMQmZho4lLVsXIJ1cXTzonFk0UgIBmbWkeQkeVMqtzHVNaEnj+jvoDiNjSeOC4kXtqImL2R3Fgp9Y2BXtrvExaHTsWyJlo5aqt5C7czpFF8K1UKRk+b1KhuSlkSxpYUlMmkpp2by/CqskAolsh5YfpzI4joOZG/bj1++vcI/J5giZJU01Vl/9ajumrNmLBdsOsdadZmRJK5S1RKmk0bHLmuWxedKospj2W9yYKXSeNEs7ZjFfNeYCVcobEbJ1/SskgiqaQUE/ZTAlzWtBopXHQlol6R5RjfP9lWmZ5fPVZXmtn57v6E1ooGkq+k3RQtzYMEpaCwEtN8zcsB/3/muJ5xra2kUupwWkmMC6BDgeFzgRshVcgljS/OBnSaMVEEdwnp9AZG0TWtI44T7wzqkiHk4k+IsIPRw4QhYn+hhvSUvZDu4auwh/m7IBgPeZ6Zi0ix+fzpAbFHK61aFcF10TVKBJpmxs3l/l/i2yHDaHmLSQJCZN9V1e/tcZ+ObfZ7sCBA3GdY2ziti2g5tfmOND5gHc/toC5pjMAhURcXtDj0igOpbkYi7lbcoX8hWTlg9lT/QOVO6O9OeRjSVN5e5I/xlvgDxp9LdWaEUmV7e+sGQjQgbe4plI2dh2oBrffXkuxi/ckVtj8oi8sjtKUj34QZUKIn0+h0b5gNkQawRzVXMjDjHwwihpLQT8Ls7kNXs919DfMPlo6TmWuNB5LWn+Slr2MWnBLWlByqIFKlrhELnM8beLWBXT93iP8YpU0BgGukx+l9ib/8SRkKSILSe0AEMrgw4cfLXpACav2Ytn6xNG831IXCgcx8GeI3XMuZTtYPoei2FCzBdUb580MR/C8j3/WoLL/zoDHyzelS5TIOXz75KvNe7jmtoQYJW0YILDjkPVnmMMIyhH0LBhXyUWbT+sLFNIriPpmyKJkqaTJ60uYTPjuyHcHWUslUFRKJmuVTTTn94Et9Rvx8Gq3Ud8GVxlxmGemt+RCIxZuzv6nGtIS1quRiTekua3mcOvSyOemYVLnvwS87Ycwv99sDK3xgREdSyJCct240itN44un7a1rNkdufHpiVFrIAVFZ+7JN9EIKycoiEOaiJLWNFrRtGCUtBYCnYWQES7qL6dvI65tvHCk4+6YfZ408fFsPmZfdkdBbI3KD17WpWI3QfZY0Mk4xSlPtNLHK4Ay5dGBjII/85u3pPHsjR5LWv27F1mYPlleio+3h/Htlxd4zuUKPysNkB/XNrKZ8c9ZWwCINxtEAloyZWPV7iNI2WxuunxZWYKC1nPosScTHByJEkZgSxZ/WZoHz/2CS2S3SYlDHPq3WCCPJVNcsuUGUNLy5e5YoLFCW9J4t2i6e16ZvRXf/PtsPDBuqbI8Wd/bnJJEn2sQS5pPOpF8IlfiEP5+P8WYU3+xcV+V9NpC4/8+WIEHxi3DPQLvnMIRh+i/T78YtIJa0uh6GmF/TqWkZav0FhJNRFdsUjBKWguBVgwPowikQX/IxK2RX5RFJBWqsoNAR4jULksYk0bT7meegzyjzm69p22CY7wipWZ3FFi7uAlVZUmT7cJWxZK+edL4OB++lV5LWvq56uLeFWhDngUH2mKrtKTV/1sI9xKhtUfwLn/3yWp88++z8dfJ6xlCjKZBwU/FY0hddjO//RR7fnND5zPxG+M0ZPE6DKmPd38JQNqS1tDsjvl6x/mx+nnLoN1Ha+JJrC+rdPuFsTrWH/tslTpORhYfxcfzyuIYdeby7QersXqPPmGDZXExadp3ZgdWGA9eGz/G/SzuTSme6L8rSgGI07HkM08a/ZhBvmPR5io7TgvXgTTnkU48cj6JVgC1nMC6zDYN7aipxFc2JRglrYVAZ05j2bAcz30k8JRXXHSEEpGCpAPZBJpdTJp3kpTGpBFrTBa7TWJXLvZY0J1XPicUE5PGlS1b4L/adFDITLW+LJOcmaet5pspsyaK4hLzaQmYtm4vTvndJLw5Z1t9vfKyyblCWK1EFkOPDuE4eGd+Oi7kH19u9mXibAjIhCVZH7HJ0tWKfZITuOXfrFowkr2uqMzdUVIeXU4smWKFuzwKIzsP1QjZ2Jq6JY1+D3/5bB2uenomfv/JqvpzwcuTWtK490OfC8ruePETX+KaZ2djX2XGpVqlAKQtaQ1oKdAk45GBj0lT5Z8E/N9TY8Q/iVAoS1qQPhbFoMnGab7BEJQ0wtRPPxtvnVWRoonw/95bjv+jiJ4MGgZGSWsh0NkpYZJ/1v/LfOQpQhxCl6snlMhyIPlBZ6dfF6KFKynZaSL9xcvUOoKTMCYtZ3bHzG/HcZh3xSufOsnFaazeU4Ebnv8KB6tiHhclXpn09Ed9P9UK2CTzKSf88I1FcBzg95+sdtsmg0jBzhVEIBRtSPi9Svp9NJ67o781ijnOWJ9EOffE4493a2PvyfwWXSJ7X3p50sQCeV3CLhi744WPT8et/5znOZ6tazePhhgqHy/bAwB4e156UyGbKmVup/xmHjtm6HeiX9f2gzVa1/Fxu4U2N9GfVzbzDp8nzW8zx289rxDEhzUG8mkZoh85F0saPxYbyojUEPGwnjqpYaSKSfMbsySR9/hFO3GkpnBjq4kY9JoUjJLWQqBnSfMKOvRH4+ZJ4yY4HaGEEE8EhWxiy2YhFJUl89kWxaTp1itaQHO1pPGuXTLlUvS3LkqP1HFxHGA0EJGVhDRDaEkr4Iyrcosg50QKUa5Cg0h48iMOaQhLmp/QRitp9LWy78svxQO7eSMXxmVlBolJ07GksS53GdQlUg3P7tiE3B1FRaiKzUaJlQm8/G+58q5fZxDhXOS+XyjkGnvEb6L4krX46J9+ybAbCgWzpAUYB37u2g3FbKjT5rwThzAuxrySpl8OfWkh08gYHc0Lo6S1EOhMRKK8Qw7zkTue63jXu3xDJlTxj/PcNH8lUCQ80cdYCv70cV5o0XlUobsjz+4Y8MvjXYdYIZojDvFZ4FVQxeDZjrc/yPgotCWN4JjW0fp65de4lrQGcnf0p+BXKzz5gN+jsjv9md8ywYFe3H82bikWbTvEnqfTNnCbG7K2+CWjlSma0mTWAjZa0gaCWNIOJJCt3nMEHyzelVOMRpN3d8yzKMSwxFHHeeVNVm+QrtYVzkOWei7bdqAaoyeuZdwnc0EoR3fHoDFpbJ976xMxLfKoiSfx2cpS1MSTmq0Uo9D50Qh05i2/+9J/O4AkdjLfyDYBd97qV2zmBmlbruNbG8aU5oFR0loIdIQOMXFI5jxRNPjkpYX0f5e1m27Dsp3leHLyBt+yRO2kJxw6DoDoVPyEpGdJ8x7jhXN60tNyReWsEPRuFr+gZ2utsR3H4/LJ07XL+kPE8JmvYGS6nN6dWtcfU1xf/69IWN5yoEpLgJFB6O6o0NJCFpcnrUC7kH7fIDPeJMnLadCLe13Cxs0vzmXOs5s3bHyR7L37JUrPzd0Rwt91iVSgHfhrnp2NX763HF+u36+8TjW28/WOCzWvqpqXzSfLNFOiEKctrLL7s7OKKCn4La8bLo1vvTAHL83cgvvfWapdtwoMY2oW781jSfMlDhH/JtCZ4/743zX46b+W4Dc5UvarSLDyqb45Ab5j2X2AdywWMsE93c5GyZNGx9wr86Spy2FdxvPRMkk9hSu62cIoaS0EOvMD68NP7qOEsaSYgr+Qk49O2Yer41pl+SazFgSz87foyBN6MWmZ5Ytvl6gKT0yaYocsaEwaXQcv2NCLbMr2Clpqd8esmuEBnUy5R4dW6bap3B3J2BU0YOG2wzj7sanK+p6ZuhGXPvklZm3MCOqkH8TujuL60+csxkKbLyuLSPBQgWF01NiRVn13Hy/djXELd7p/88Qhsjttn4VetvjLklmnJEIbXX8saWeVtHWVgGCHhorYoUm5OwqPKb4dybkRz8yS5sNkN+3Eu/MO9GIVhW0SvOeNeytRGVNbgFKMGy577mD9mrHYJ5+fCGtLK/C3KRtQTdVvBdx048Fb0ug5I5ZMeXN7+bAT6ihp7y5If8OfLN8TqK08VF4heU1mrTFviSBawxsqJi1om/Ntk0wJ5DeCIH2gcpvMJ4whzQujpLUQ6Ax++uNz3f2o+4iAye+IF9TdUcOSprsQ+CWzFrE78sqVjpCns2jSxgEdoY5350oqiEOyjUlzHJap03a8ObW87p/pv+uE7o75GRc7D9e6v8mOs2rIuekTJPWLBGzHcTDqk9X41/zteGfBdmw9UI3bXvXmdxOzO6osaRZroXbys6PKl+GrpFFCYIqLIROWryjv5+OXMX8nuDhGqTAucU/0awstwLKxmew8RLeBIB2T5v+8PPwUJJnCAmT3foOkJMi9XNX14uNrSiuwvqxSeE4WH8W7mcr63u+d8IJuZV0Cw5+aqbzH+93lb426+plZePaLjXhy8nr3GL+ZFRQhj7tjuoydh2owZNRk/PbjVcx5pp8F9ekoacR1nEd5TRxXPzMLL87Y7FsG4GdJy5/akS27o9htX34eAD5bWYqPlu4K3khF3Q1lSUvZDh76aCU+XLJLms8SCMZwSZ8urJJmtDQeRklrIQgak0YmMZFbE78DU1h3R//juiQcImWIJj0RxaRV1bG7taJ+5KsXTTRbD1Rz92Ru4uPV6NszqRBYQVNFwc8rlrpIW9LYeuhFNpXyWk1Js0SWtHyNCjoPn9sNKkGz/t8gmwczNuzHG3O24bcfrUJxJOw5v6a0Ast2lkvcHeXlWlb+3FFpyCyaMtDGKLp+WVOCuLQkkuyY0VH8RO2VfetRakdDZO3my6N/V8eSWe3A+22c1CqUNBWTrdx923usULKQ6slU53RyVsriXHRSZsjAC+c6iZstFF4oXr27wv1N15SPmDSyFj0/fRNiSdtN6UHglxpGR0k7tm1xpgyqkNdmb8Xa0gqM+WydVtt5BTMXqDY/so1J865ZjnScAun58af/WoJfjF+Og1Ux5AKmzRrjIh+Wx4krS/HO/B148N/LmTo97o6a3yfAEUkVUt4rWMnNF0ZJayHQWTfYmDSn/t8MxMQhhf1opTEzWVjShBT8lDAmYnfkXWpsB3hr7jZc8Jdp7jF+gRX19dId5Xj445VYuuMwANZFTkX0QdrB7/ylmJg0tsJs3R3TljQ2V57HksY9HOnTWkEyaydP40JEAKAkPyCKbYD695RnCARkgfA3PP+VUMFS7RaHQ5bn/ebje5G9B2k7qBcZ04iRCyJo8t+NrN/Z+Aw1rT8N2t2RtoJK86RR99Z6YtKEVXjg945ERDmZOvy/Z2996hyO2UJUQrYKk85czCgr3DqRbToVvszdlGVdCs6SVmjShlxjdvg5h3xT+yrFSoLfxoOOktaxdZH7u7QiM/8FTZejIg4JQlIzcWUpBjwyyaOQEmRrleJffW0ihQVbM0RIfFH0Zq1qM0YHrIt3w6ggtGJJ1xlPsvX7xTWy14rlJIPCwyhpLQRBFylyuejj5N1YUgX8aOWCQea3SEgWCRsiQUiau6f+eCVHZew4Dh6ZsBq7KEGBz3EjavKCbYfw9rwduPEfczzXqATC17/ainELdnisl4wljRC62A4mLNuNzfurPeXowHbYBZpvVTomjZ/o0y6QS+qVT768fMAvNQQPciqIMlQVy7xnFfGDUElTJPy2wFLUA9lbOmmIdodVYCy3AoIcHkEEigSn2Ou4KE8XEHPI5ihawYxL2i6Lr+AtabrzoN87UglvKiuc1OVPFKNXIMVC59sRQfZcdFexXgDsb1nZfs/JC+e7y/2VNN6SRlexaZ/YbTMwJLpJPvKkkY22/RIlzc9KuWZPhYeRlQc9hrdSa0Y0oGVM5e4YZA24519LAAAPfSQmMqEfM8jczr+PCcv24A//XUOVy8/Pmb9l6T+yqbuh8qTJNkpUedL8mkbPT9mGU+jAeDt6YZS0FoLAShq5j3Gr8xKH8ApDviFvd+a4aE0RxR35JbNmSq8/XOFxd/Rey7PPBXUtVe1M/enTtfjNhysZlz+erIVMmh8s2YUHxi3DI1z8gi5szpJmc0qZjDjk79M24f3FXv99+t6HPlqJbzw9U+nOIkOKI6YA1MIkGbNBFI1K6j2nFIuQKH8R7267ek8Fc87r7pj798Iv+P7sjpnftKVVJ0+aH/iYO9E3tf1gNa70iSPavL8aN/3jK4+LEV0a03aJpYSuviYejN2RwG9OU41j1b2yucFvAymfUJWqqjKoJc3L7qiea3XalLId7Drsn9CaZ3ckde84WIMr/pYZh7n0MJMbjWtjUMjypMmUNKZPBB04a+MB3PziXGVf0cQn9GakjKhHBpW7Yz7HcLZJ6f03Adi/6fCDXN0P6Xbq5UnL3d+RHRuZ3/w6xG+2q8BsZhcyJq1gJTdfGCWthSDoukG+SfrbjQvcHXmmwXxDHmxO/SFS0uop4f82ZQNueWku6hIpMQW/VElLH9eJSeN32/iduSLBosdaKP37L8ERerAxaelnXeizc+oHm1PCbIdzURO8a8dx8NRUcfoD+sp35u/AurJKTF27N3C7REmLVYsKORNk55JW0lTuonUCJU21roZCXiUtG4r2X4xfhltemuv2f9D8ffR5WtGkXfNmbtiPV2ZtUVrDROAp+EVK7iMTVmu5YC3ZUY6/TWHHE/2uY5RyxLg70lY16t6aeMpXoBXBb04TufcSiJS0Cct2Y/RnaxWWy+Bt0IHocdVWV/k5nZi0unjKVSx4IVBWbVDikF0a7o4WLCHj56xN6tQKQUB/99mQ09DwxKQRS5okJoq1DsvL3XZArqTRrvx0EbKUFzIoLWl5lA1k3ie27WDeloMerxfRfeLz7AXMfJ1j85lvoIG8BJmNEsVmsG7MKMCzOxZO3jPEIV5EGrsBBg2DoINfRFiRTNl4c842xt3EQaFj0mTtU98XS6ZQGw/h2S/SSa4XbD3kG5NGw41J4yZ+0QLM74LyVxRHQh7BX5WMWuTHn+QY+USBvOGgGbI58G3kBSveskau6dK2GAcEwoRozAUVPA9Wxbj4Aae+bPk95FyQuiqo96yKN4oJLCgqkYbPkwYEp2hP2Q4+WrobQJr++5Qe7QOzO9LnWSUtc83tr6XZLAce154hFfCDagOBoMaHLp1GFXctPY7inKU3U69XkQfSCXvp70l3HvRTpNXujt57Hxi3DAAwtFdH4T0iBaihLWl1iRRembVVep8s1o5+3ev3VuKsx6Zi/kOXezwuZH3v62rFuYyVHfFPQJ22pHnHhMwypYMdB2ukGzL0I2SjXPMWFFG6Gxq6sU6qmDDakkaXJ9pUVEEVk5ZP0UBG9vGvBTvwyMerMLBHe0x84ELlfX7lApxLdc5KGjt2GwKOpE6RF4h7j+BYImXjf99ajKG9j8E3BnVnjjcE0jHxDZMovSnDKGktBEGnB4f7F0h/nKM5xideYcg3dGibRbJDLGljHWVZKo6EcKRWX2nIxKTxAqP3Wt6Hn180RTuTqjxnIjAMkI6Y8ITfjQ0Kvh2O443ZE1lwurYTK2n5GBYXPj4dNXHa1bO+bYp7yLkg45K2mKoEcJGbm4pd1LIsT78GXeQqKAvU7a8tQFVdEk98ewhzjd+zytxrRfftPlyLTm2KPMd1IHNr02VgBbzfGD304xLiEJllobIumZUrWi7sjqo6DlaLlQQxcYiyCQxiyZSQlVQEWfOen76JyX/Hg+6TFbvK0aVtMY47pkT4vudtOYjjjylx/07PJbL2qPuaV4BUwiZdH10sEVyzVdJq4ylc9MR05hgdC61r2ZKDUxRStprghfqt9CqQ9bntMPMqfR29jqRsR6mEAX5KWv5kA3bDOPP7g3pX+zWlFZ57AH9rHt9E3jMgFwR1tc6HSsJbnt3fgg1W0W+CKWv24ot1+/DFun0YPjCjpGXjCZINHCe4u+mKXeXo07kNOpSI00s0Rxh3xxYCeTyA+rifW55s5zxf0FHSRDtUdYkUvtp0wP07aYuVSdL2r3VpwxzfdbgWVz0100OdL6bg52PS2PMiH/+gbEkM2YPDsjuSBctvMfVDjKPRd+B4BFx+CNiOg67txVYXUV8FXfNqOKuWW6ZSMEmfCzIuaUua6ra6hMjdUd7v9JgjVPJBvxfaTfBQdRzxlI0lXBLeIHEXsaRaCHGc7F3tZHkTgxh56bv3VdQxLl+sFVAsZNBvKJa0pZZDFfyeX5QXkGDnoRqpYCgXmoO3gWDx9kM4+eFJeIpzE520qgwzNgjc+ySNmL9F7S5N2rNpXxWue+4rnDcmzXAraiYfv2o7jtSqs3L3ER/qdVbQ1Yr55a4hf2WrpB2qiXuO5dPdUaQo0HNfRy6nma7wLztTHZe78bNsqv4xxPm2pBVHQjhcHfeMCYcbT+5xn/KCujvSLIi5Sja5xipmAybRORMTx13ns7FAj7+gm8rZIsh75TFzw35c99xXuOJvM/LbqEaGUdJaCGTzg2w9IYfp+0TKhON4rSv5hHRioycYwTWxpM3ELsRTtrAsMikURbyfwvq9lR6BU+dR+Umft3AlUraQnVEFnuFQdH+ulrQYp4DYDvssS7Yfxj+mb2KucZy0JU2EQnp3qIom9QYZl+U1/vFSgNiCoup2WsFqFU1bOoJa0kSxXLyy6DeEGCpmhoJfoKTByTp+Ih3H6L05yAYCEXjrEimc/ecvsJhSSEUJ50m9ot8Aa4nUZnf0i0lTKBXV8RRGf7ZWeO5hCamPaA7Qdc38/SerAQDP1Lt2E/zv24uF18tK9XPHIn2yYlc5W57ErZlXomVd+t8VpXj0P6ul9TpU16Q3ivz7xWvxT/8tivGqS6Tw6YpSlAsUMbcNws056jx1PBthXORyp2IZ1LXcycZ7dUw+fum8hPyaIILq084mxiiWtDH0j1Nwzp+/4MrK/Gb62M8SG2ADC+AsaTnKNoFJi/JgSmMUQ4VbLL+JQlBZl8AHi3dJ583CJrOmfwfr+0mrywDk5tLcFGHcHVsI5ElU1Tu+9H0iwWVPeV1hLWlSd8TMb9HkF0umGMEnkRQraWTCESlpuu3xm0x4d8fqWJIL6PXvP0JPDHhdTAmhSzhgwDcPPiE1odcn+PUHKzz32LbjKh+ecyIrTY57k6RMNXFI+pySZc92GFaywwoBjYZox9+y9BaU1kVhVNYlA8ekiZQ0foc725g08Xj2CuxRzbGVZgj1lhnI3bH+X1HsUUxiSVMlyqatpLoCtC9xiA9L6T9nbcVvrxmoVRcgsaRpjKn0JpleHSTGQ1as3zPreDXQZTEU31DL0ioyEHrOSNl6z8t/+6RukQD35Ofr8crsrRjSswM+ue8CcRt8XoVUgdAEf0c8pVZGc7Xc8XGfsjJELMk8CuXuyM97MoXHr4agljQ2pYhP4T6QWf8KCaZOibeB52/q5y//vRyT1+yVXht0/coWDWR4bPIwSloLgTweQHbc8ZwXxQI89NFKtG9VuGEkZRSjZhWRMB5L2Ix5f9fhWqGgSWLOin2UtGg4HVskVjxY8NdEOV+vyrokx5bE5y9RNsXjUkaICvh6goLfNXUcnQUumPBWUZvMKSCYCGiqPnItaYqLkraDIkq44Fk8ZRC6O8LSWlBIzBBvNUmmbGzcV4WTu7UT0lmLlTSOiEbyrOvLKrFlf5U0gFwkUDqC47pkArLxEERJIx+U6Jlkyax5hYBGRa2YIEEFv91iFbkMALQp0osPI8g2mbXq+xNdG7bkGyV+5fiRLNFIcZYzx3GUGxmqODN+x1+nX2RzqkhJ+2T5HgDAil1HfMulQcek+cX3+IG/JZ5MKRURmRXEW7D4cLWHnCfzm+46HXdH1bedT0FbFuLg191+mx38uIznMSaN2UjSikmjxpTtwLKC0/IzmxqSPuNBPyevoKXPZ36r2I9zhcP8Dtb3DaQDNziMktZCEESQpsFa0sQfJ59LLJ/QUS5Fz1CXTDFuV3TyShFUlrSiSAjF4RASqaSwPXz13pg0zpIWTzK7wbzwo7MzSDPI5S8mzZvsUsdCE2QD4PefrMbK3Ufw5LdPy6qNWuyO9f+qFkX+uXStwby1EUjHW+ks5mQc8Gvcbz9ahfGLduJXV56E+y470XOf2N2Rj9cQ13/V097cZH550hyB4F8ssZZ673UgkreDDE2yOIusfFJ2R84dmIafJe2zlaU4UpvAd8/urbwOyPSznyXt9N7HKM/z4Ptb59sj1+kKJ4QEQmaJ0iWfUSVvd49xsWO2rZ7XVEox782hY2H0MObW3yOyDOkkLBZVKXN3zEYx8Vpz2P7jv2/euiiDTNDllTTZehqr90CZvm4fTut1DI4VuLY3HHFI5je9ZvoJ80HidQGerTa39gdldyRjKpmycfUzs3Bsu2K885NzsfVANSrrEhjS8xjfMmQKt8fdUTFn8mDZqAunDbHujoHvzmdTmgxMTFoLgWzA+x1nYtKSDf8RyAPw1YtUPGkHcsNUMaO1bxVxJ0+hQKJyI4CXGr+qLqlMZu3vQ89a0hJ5iknjBX+dXXrVDrnsuCjxtS5IkapWkWtUwgt/TtdFSUQYkbakaShpIUIcwr7v8YvSjHpPT93ouQfQtKQF2NxM+FjSRNYKP0uze68ttnQEi0lL/yv6fonFZcv+Kk+cZuY3W9cRn5i0n/5rCX7z4UrsPJTJKbX1QDV+Pm4p1lKMcY7j4PbXFuDGf8zxCLm5gn6W+99diiv+NkNoteXhQJ/a293gkJz3+wZIG/nqRNXzsWO867SsbBFY4dzWihOKB9j40skL5qcI5OruyCPOuefzRTJkv4rqZAq5Ks0Fo6QlbHywZBd+PHYRrn5GnIxeqaTlyb24vpXCNup4nqjAt5GeH1ftqfC1qidTNkZPXIvp6/d5y6bnpQDjYl1ZJTbuq8KczQcBAJc++SWue+4r7KvwTz8he5cyMp30PfplFpLdMddwiKMRRklrIZAK0pKPwoFX+E7YduAcKrlCnkCVuka44663E02geq7iSNhdiET9yB/hL+HdLKtivLsjr+Sp22o74p2tXGPSeMFfRqfOt0U2ZxfC/SCWsnH9818xwrO3TemKlUoat0udiyUt/a3430uUdVm7ZGO9QsOSFmSsM5Y0WcynREnTCcIX7bQGcdlRKdmkXQ99tJKrlxJMuHsY5k6FfEGnkdhyoBofL9uD65//yj2WSDmYtfEAlu0sx4a9lcpnYARsjbFFX/+f5XuweX81Zqz3T7ys843ydcjeob8lTZInTXBfUhSTpihbmcOJmyt1lFJV4l4eOptb/lWKBWNdiOKiVKQT+pY0MbxeE5nfdHmxZAoz6xlCD1SJ43bz4e7Is036lSXKgSe9z49Uibufnh9/9u5S3P/OUuX9S3aU46WZW/D4pPWec36byfw1BHSf0vftKvdP5C57l3w/BHHRZWLgubGztrQCr3+1Ne/slUG/I+PuaNCsETQmLe3yxB5LpJwGy5GRboNc+PVjTeLZsfygcncMhyx30tRxd+QnXV4IqOKIQ/g+1bFeMZa0+kXFypEaSmhJ813g5EJinudsAMDyneXa16oUrxSXgFkXIuuGbWu6OxJLmmRcOk6acv7v0zbhf87tjQHd2wPQtaTpP4SfO08iZWMelUAcyOyW+ymzDvwVOT8QAVT0TERA5+OHVDvrdEyaZzeZc6XjwbBJUiIvnxqCB/3d6CgVQlIjjbnWcfSFE792+J6XKfSSOZG19qiVSaW7I/U7aYsJoHh43R3l1+q4O/qlXqGblBVxCK8oJG2m/7zusFR9eVAKZUpfLGmjY2t1zkRdd8f9lTEUR0No38qbw6pGwjZJxy/L3Jv9ECSkA/CORcIaKENNvYIp2kzTiUmjD5OepPu0hlJgdTbJ6VpkaUr4euk+SBNhydvIz5NXPzMLQHp9u21YX9/2ESRSNrYdqEb/rm3dd5yLu+PRqqQZS1oLQdCYNJHwHUuk3I/1xK5t89o+cRvk5/wWqXjS1hKcCfyUNDKJiCda+eQHePOkVceSnJLF3e+zAPHuaKSsXP3nPfEaupY0DUW6ISHK8ceDCagOsOCL2B35fHIy8IrO2Lnb8If/sLGSD/57Od6atx3XPDvbPaYXk+ZfvwiiZ3/0P2vwLEfn7rog+mx8yKySQRS3jLujnEyjc9u04EgIOlh3R/YeVUwa686tVoroR/BT0oKOL2EOR41NpvRmml7fEip76caXpruj5z5BgSmHd3dUj1EVwy3PLKcjoCcU75lH1koa9dtx1Nf6waOkpWwlOYYutbvsu+PHli1pfyyZQkcqsX1QV2ZS1OHqOM56bCouqM+tx4N3vxS3K/ObsaRJayf3+cxZ3N86ydJpkL4UWQMZ5T2ADEYPSbpvdFioZWOxLpHCpn2VwgvpFogsy+w3KO6fZTuDEe/89O0lGP7UTGn4Q9Cv6Gh1lTRKWguBbJ5yJPORaIeWFkxktOsEvTu11mrXf1fswTKBdWTeloP41/zt0vvYoHSRkpYKFJNWoniekJUhPhAJTp54Aa7jeHfHyjrWksYvCr7uGw6XJ61+0szV3SAmsKT5tUUVk9Z4Slr6X5WQyyb51G/n6j1eN0sdQfmOYX3ccUDcxn43YTVe+2orc93y+hxUdJv2CuIQdNkd/aD7jZDn87Pu6LLvqctI/xsXxMCSdhCL5gnHtmGOAwJ3R0VMGmM18Ik9oa+t8XHPCsr2J1TSdCxpCODu6LN5oWtJ0yEO8VDwO2pFXSb4pcvP/NZ2d/QI2vJ7dNJL+MV8sgmEfYvzQOTuqLKC0H+qvmHZGY/7JK1M0OyOCZthbz5U7XV5DCvdHdMFL9iWtszLSMZk3xPDcClZ84PmQZO1kSBosmbS/yJroI67ox8bLh3/qhPaK1PgD9ckcMXfZmLeloP114nvESndopQ/PIJ6WU1dm2aRfHV2Zg1k54xg78FY0gyaNYJa0hzBOXpHx49I4KRu7bTadd87S3HD81/Bth0kUjYe+mglPltZiu++PA+/m5BJcMpPHHTLhG5KmgHmAHDtacehTbHc8zcSCrmTZjylnogBdvJ77MZBHuKQ2niKETR4pjgdRkU6PoQsKjkraYKYND+BKFt3x3z7r9MgJftR8Gd+5+bC60C+2QEAbYsjePT6QVoug/yYXbGrHEt2lHuuU7k7ztl8ANPWeWmUdeqTgYwDP2uT7eSP/UvkAkeek1gSWxelv1uVm4zKFY0eI3476PS1MkuaSzAUcBOAXMO4Qeta0jSHrxuTJjnvV04gS5rteIRAVTfIBD/AS1qQbZ40YTyx43g8HUQQvUOG3ZEeY9lY0gT1qRR9v03KTLv0FHJZXbGkzTybKIWBjrvjPp8Ew7Lk2owbM3U8yAZscHZH/wEmCleIp2zPHKKzWUMfJmOK7tMqqm90Hpu+RFTnjoM19dfRbcucjwhS+bAx8OL+yXbep92GWStgVsUddTBKWgtBYJc0R2RJSytpluXvIjKgu56SRrDlQBXeXbAD78zfgZ9SiZsJeF9sv2SeQdgdf3TBCcrg8VDIcnewRBTOnlrq23PHsD74n3P6IMqVneKsDSo6ZBEcThAmi0TuSlpwdkeVu6NqJ8zPEpELtIhDsrSkieAXb0PY4yI+xCGAV3j6bJU4HkJGwZ+yHXzvn/PxwzcWMUQYuvXJQARjv29KptgHk1vrFUKFkkZy+rUm7o6M1UFessqC4K+kZX7LlDSSq5Bx9dNQKki76Pt05i/ZJono23PZHWWCu099QWLSREqGyh1JTcFPX5c9cYjs8XQsaf7ujpnztfEU9lX6s/AxqL/9l8NPAuCl4OfHkK7CIusqjxJL/aa/pVgyxVjP9wvmFFFuR7es+qJoVkLR+BMRMqXvF6/zQfKkBbek+X+wScn6wedPZDeI9OrnQefw1LKa+1jviMcAfYp+JyKlm+4T2XjLdrOTro4pOeCyfLTqdEZJayGQu6RJrhe40ZDdrmg4BL+8yccdUxKofYu2HXZ3eETgfbFZ9wyBkpbSd7sKWeqFJhzK7PYIlTSuGlItuYeneOZzCPG+7L6Kkc3FpBFLWo72fp4Uw9EQbLK1pPnF9OQC0hzdPGlBdmVl9emwx4Up4hBdgZcoI53bsMH7MktaeU3GHUm0683D7xshbsCuu6OPAOM4YhbAID1MmiSqi7hTEga21qKYNGXZjvTvT1eUqtulodiLcuFpEYcINhZ0hEUH4rEnqtLPkpYtcYhMIWRd1dTzga6SlkzpEYd40ppA/o2KLAc8/IhD6LP/+/ZinP3YF8r1TFZ+NJLZyFG7O9JjMbhwnFKkfaHHQSxpMyRLOnOKqNx9FZn7hEq9xAojS0PAJvpWjwc/tzlVMmsZ6PdBu0fya7kOyyt9mJB/0U2iPZh0E9wTiNY24g0hI1sSbVjT5cg2s7Ld7AxJLGkmmXUaWSlpO3fuxK5dmWC/BQsW4Oc//zlefvnlvDXMIL+47rQeaB8VLOaSD0FkRSETUHE4pKTdBYLn7Fq0/bB0Nw3wKmm2kyYEGDt3G/ZWeBcOPs+MChYspV992LJcpVQ0QckEP1Ik706T5BZg3pddZ2dQxO6Yb0uaA/kCRwQb29Zj4OSR7zxTNEitKqEzaTt4ZdYWvLtgR+6WNKgFUNJXdJ40mVDK9zcRwHj3Yq9bTfrfw5SSRv+WwY+WPUIp44C/S4tt504cQiDaEEnZ7DgtiabdHXV31lXujn4sbjq72GEBE50WG2H9NXTfzdp4wPc+xxaPPZkLIpA9cYg8XYP3WJKbF/zSVCjjRxmBWE8h8RBj2PLNJL2YND9B33tM1+UYyMxZxEMlkVKzO/oJ4ny5PPjuZtzMaEtawmZIWERKmurbJs9AWxbF8Zf+Y4uPtSL1+n2Wfu/OtoF35u/AH/+7Bo7jaOWDFcWEA14PEZnyy9SvsHoD7FqpZUhj4iO9N7hKKP3OA1nSJO6OWa6jbFJ4vXlcBEMcQuF73/sepk+fDgAoKyvD8OHDsWDBAjz00EP4wx/+kNcGGuQHdwzrg+v7+FuBMse9VhSiTBRFQr55j0IhC7N+fSmObVes1b6dh2pQG5cvwLy7o+04+PV7K/C7Cavx4ozNnusDKWmWOqEpTcEv3EXiqiHVkns87o4227ee3TdfF0OH23F33HJpBEhNBcBrSbNtOZNaRniXC69qJa1wljQyqFWC3+7DtfjTp2sx8sOVQsEvWPJlOXkKXRYdkyZb0Ph3SMaC3/dG+vpQdYYk46Akr5GoPtmnQr470kV+Li22I98VJ6CJCEQgfSkK4Lcdh3EpIpY01v1ZXrZHSQtghNCZTohFnk/k7Fs2cXcMGNfhQPyNqhS3bIl+ZMq3UMjk3R1ttTAfT9la7RIp7iIQxcLNbwn5u84Hu6OYoU9/DiH3F7mWWC87Jg3Gk0QZzyc+zlvfZK6EdYmUUglR1UGXtZexpMk3EGT3p+vhN7Dqx7O8+vr6/M47eOijlXh19lYs3HZYuB546mbCDWiFSu7uKJvzRfHM9JVBLWnM2BBcHhdY0uhihZY0qiCZEsuvtyoyIBqytS0w8djRqaNlp6StWrUKZ599NgDg3//+NwYNGoQ5c+bgnXfewRtvvJHP9hnkEaJvQUUcwg96sgNTFAn5ZuQKh4BenVrj6kHdtdp2sDoupDcn8Lg7Qr3zHU+ltN3/QpaltAyGQxlLG0/yQdrC/u3Ul0vuZ9vOCxremDS/XVs2X50sJs2P3IWHKCZNtv4TwUbl7qiaNHWSl2YLUq2qH2ladpHiHSRpe1pRlZ8nCm00TLkySTqWL4e8Uz+PLPKstPXsoEZMmp/gTtquUpz48oQxab4toctAfV3e95JMOairf19F4ZDH0gdk3B1FnzTfNnoc+LfL/ymIYJ69JS2Y65oj2SRRWtIkZWWTzFrmxihy1/PrPqkQS/3WVtLqr4u641fh7kh965V1CTw4fhmmr9vHXCOSN8n4chxxn2aTwJ3MEby3RfoaseKvJCKSPLOIWIWAYXfkNjtFY0T1XZBzdCxbECZT3mWWvUc9d+m0L30+87uiNiF0d5RtngGsMqIKXZC7O2aO02OKgFbStDZ8qEvEzNeimLTMb1HoB7MpLLWkZY7vOlyDoX+Ygj/9d43wWhr0/jhrfTcAslTSEokEiovTFpKpU6fiuuuuAwAMGDAApaVqv36DxoNoyZDN76pFLRoO+VLBEqXHzy2S4GBVTKqkhSwBu6PPZBUkmXUopHbPDFkW2pdE69vptU7I6JHJIs270/DCJx2fpUrgnamP29lKpS05/ELil4SUh4jdUdbP7g61owqIltdF78iOX7gDPx+3VNuVyQ9BiUNECaqLo/pTowP14imKSdNJVEy30+87ItcdpiiyD2hY0jI59sTnXcXSVdL8YtJyp+AndwuJQxzHnSeKoyFhknnyu22R12JHK8dvzd2GCx+frt0unfxJpD1B86TZtoNlO8txUEBxrrxP4PEAiK0bGYVcVpa6LpEyIJsjUtw8plpPCHRcgPmNJHlb65W0+t0NFbkPPT8/NWUjPly6Gz94YyFzjfgdWpi6dh9+uyiM2RsPes4Gssa7bcnMO7wlQqaw6MbeMscVbr8219/0xoxoA0b1Xskp2votKkOHnp6/xFXSpLWTNqivoM+HQuLvnB/77CZp5pzXkuY/D4hiw+g2VWsqafGkjQVbDzHfkahOcp79PjN/iGQher2SyVX08eenb0ZlLIlXKHp9GVh2R7lS7oejValT+51IcOqpp+LFF1/ENddcgylTpuCPf/wjAGDPnj3o3LlzXhtokD8IlTTF7qVsQtBxdyQLlK6SdrgmgUpJnBJtyXLb5/NFBklmHbIsH+IQC53qiRtEOav4akifZmLS2LL5RSCoz7nteN3lePcYADj+mBKUHtFnGRMpyVLBhlgMFIqcrrvj/32wEgBwXv8u+M6ZvbTbKwOpVldJEwl+KitkUSTEvENfdkcuJi1lO9obCOQ9XzagK17/apv0OlL9IdqSVq1hSXOVNPmGDH2dnyXNgcwtjr1GB8L4TzujpLWKht3NIpGg06Y44plT6LY9QqX48MNHS3fhF+OX+15HZGwV6YMIX67fj/GLdmq3h0A2T4sFYXKPuD3+sTsiAV1utWPeiUbEiGxs0cXHBBsqIpDvKxoJATEfd0fKTL39YLXwGtk7/Ok7ywBYmCGIH1TFOfMgcyi9Vng3zbzXA/mxpNHgN7BkqQZE7ZLVz1jjBO9ZJ70D/yxJQWyVuAz98xYsKWERDYa4i/rNu4Oq4goTKRu3vjwPXdtnQkLIV0JfyVrS5M/x0EcrPYmhRfNATOjumPktjEmjxqJsM4WOXdR1dQRYdkdmjMPBH/6zBuEQ8NtrBvqWk03cc3NAVpa0v/zlL3jppZdwySWX4NZbb8Vpp50GAPjkk09cN0iDpgfRmiEd15IdWiDtZuS3SUgWqABeYyg9Uis8LlKi/Hdl5XE/PCyoLWm0kiaiVubFD1ItUVB59jCVJU3XnYEXYJMCF7rjOwZj2BRZ0mRdGNFwd9S1pBHoxFDpgFSrcndllTSBu6NCSRMpcKrXJopJ07UaEsH4+GNKsOSR4fjXj88RXpdhd8y47+2v1IlJS/8ra3+Uij0E/Bdf2xG7cgaxrqlcK9OWtHQbWtGWNFopqv+3TbE3QX22Vr6F2w5rXZctcchnq7LzQOHdCEl8pMrdUaas+LmHyyxpMndH3rqZrSWNLkfX3ZG4rEWozSRZ/WFKMdKNFQXS6ymJiRRBtfHHgzSNdrNW59yi2ybvE9nnqiLQ4ePTZHTzmbarlMRMOXSZPGSbVrJk1oD4XY3+bK3SAimsgy7XkrhZCzZF3XO0u2MAS9rsTQewaPthTFyZCdsgl9P3yWLSlu4ox7DRX2DiyvTcwStofNsIRO6cdNNEbKcsUZnsXfnT9ItAb/jT72JfRQyvfbUV/5y1lemDloasLGmXXHIJDhw4gIqKCnTs2NE9ftddd6F169Z5a5xB4aESsGWTb1STOATQt6QBwK7DciWNV/b85gDel14FS8OSRijQywSWKb6b+Jg0XgHkJ0ndnTKCiroENu6rZI4lUrZn8Qvq7shb0niCEhoZ4UflMit/mCoBcUjgQGEJXEuawupDLyJCJU2xu1AcCYHufVU/ARlFh+ySJ1P6OfzIdSErvVEgEwozxCHBLGlkYfWjJ3fZHf2sLY5YCGMEH59HJ7cLd7RtB7H6cVoSDbvzUNJ28P1X5qNT6yiK6u9vK0hQny0Dme7OcLbEITq030Jwys8tL8+DBeCl277uudQ3NsdPmCUeApRPxl8mrcMny/cI6+L7wK8b5Eoa/a1qujsSS1qYuDvKn59Pli2CaExbAHoeU4IN+6qE9wTZoCRrBu3uyM9LrDWabrNCSdJUOumybUYxYoVvsZImrT4zb1DvVtQmmaLJWqLYc+Qd04dfmrEFvTu1xv+c04dqX/qKSMgS9hVdtwUJYZFKSVNZ0mhlmrekCZmiSZszx2Tujnf/aykO1yRwz7+WYNuYazxl8W3j62Ut3RmI2R39v5GkhEzFDzICHhVHgQj5kR6aHrKypNXW1iIWi7kK2vbt2/H0009j/fr16Nq1a14baJA/iGPSJAuXwj2lOKxBHEJi0jR2E9u4DG2SsoTujupPMq6ZTwdIK1NKS5pFuzuKKIjFfxNhhqfg53dIg1rSZm88gETKQa9OGUtZMuVleQuiIAMCdkeFok4TSmTjUlMj2Bnjd1OzdV8gI1dlGaDfgWgxUDG+eROrq9+bkN1R15LGuUHJ2uUSh9BKWoCYNFnr6bxNgH9MmkxhDbJok/cnS2ZNUnW0ioZdQXjTvirM3nQAn6wodQWdNgIlLVtLmp+bJ4Ebk0Y1XedV65bPwwE79hZsPYT5Ww+holbwfdkOnvx8PXaXizfDsrGkyVxwaYp0oJ5cw8+SJmGNY9wdNS1pbkwaRRwipUCnipRZdESKhWWBmYN5BJl/SRtod8d4Sm6VYS1p8n6VPbPXkia+x3FY12xVknQR0vfbSmUFUFgwuTHE3iPeYNq6n3VZJUXLYgTpcWdZlnDDxBuTRikkdJ40hSWNH0OiJ3bdHamTzCYu1TSdXKPiHLLefttfWYcJy3YjnrSFTNf0eiVbA2RpCfwgy5PGxEJqzNtHqbdjdkra9ddfj7FjxwIAysvLcc455+Cvf/0rbrjhBrzwwgt5baBBYSEb1yrBsyjinyeNuJDoeHx069BKeV6UbNrvg4wnUwGUNHWetJCPu2M8ZePutxa5O16k30iTeeIQFbujzkSz41A6SeqlJ2c2RBK21zoTZCdXhPQYEJ/LWFjkbVb1f029YqRKypqtQO1a0hT300qaSPBTpWQojrLWLFU/pcsiMWkZhUdXKCcCgJvOQfJSyZpYXktT8Osns/aLPSSn/WLpHEeyextg0SZ1iWLSGHfHSFg4D5G9htYC4pCg7IlumZrtD1PudQQ64zjbsS4lDhHM7JNWleG56ZukZfnNPRl2SP+2Jm2Hs8jIXacJZNZExpKmGZNGxg759lR52nSYEmXvh08yTyOIkkZKT3uNpO/jx7/MOpsXdkfQ45W9n3F3FCpp0uph294NCFFfSin4bfEzA8Dtry7Ayl1HPPfw48jd6JIII/z1QguXwpJGW3e9ljRxvwLi703s7ijexNXZ0BXN12Rc0bfP23IID4xbhr9P2yi2pGm4O9KKWZBNObo+up/pudrPyg+wsuzRFJ+WlRi3ZMkSXHjhhQCA999/H926dcP27dsxduxYPPvss3ltoEFhIRvMKsGzKBLypQR3Y9I0Fqru7dVKmtCS5iMoBMmTRi+MwvotC53bphdj2QT1+eq9eHnmlvpdw/Q1xBWL9/HmF99Y0nYnuCAuf93at3IVwGTKSxwSJCZCBJWVjKY+zzwve42OG45KAMhSbtWii6cXVpEywKdNoMHHpKUFQHldRDgg/z43fRNmbNgvvZ4GL2DIlEdyHf0s1Ro7ra4lTUaqwLE7+uZJsx2h61IQS5GrpEncHWl2R/KN0d9v3E7/zmdMmq6SRpoRlDgkWziOWIAR9bcs5lcXQQSvdJ406m/H3x2JjK2JK0vxzb/PwtYDaYsIa0nTZXdM36Tj7ki3U4fAguDz1Xvx78W7pW0I8tYdag6NSJQ05hulCle5defD3THJuDvK2y7CpNVlmLyGTZUjS34ugkop2XKgGje98JVH2fHG8qX/la2HtHKRdncUbw4xf9cX+sjHq/DPWVvd414KfvoevlyBwivoB3oTVzetEIFovhZR8BNMWlXGEOm49TLsjvm1pNFyA90mhqUy4HPz43vGhv2Bkss3JWSlpNXU1KBdu3YAgMmTJ+Omm25CKBTCueeei+3bt+e1gQb5g+izkcYTQT7BR8MWE5cgAhGadHLFHNM6qjwvUqJ0dmWDJLNWKmkhC53a+Cfl/mxVKQaPmoy35+0AQBGH+FDwAxmBOohAFw1brgKYTLGEDX+8YVAgdjERbEeRzJqJSRPvVOqwK9JWLH6hzla4JbepBDrWkua9TuX+ypOK2IoNDYByd6TGwV8mrZPfQMGNSasvQxYrR/oqaJ/5bQ7wecjivnnS1LTPgL/wqnR3dBw3VyHN7khbLYj3aiQUQisulUKh3R2zzZOWLWSEGCLhLNdm6OxmE6Qcr4DtSxxS73Z2z7+WYNXuCvzqvTSbJr0hp+3u6MakZcav7D0wMWkS4TKbkMEg/UWaELIsd+7hn/WLdXuFlm9dpkbVccajgXOrpMe+buJ0Gg+MW8bWLRgHQejpaSRS3sAMjyXNFq9PdBkEliX+1nmLFGnvW/NYebdGRRzi8H3ubYsjOFdVR3vaqPuDh8rdUTQTOxDLQnzKH7+6siUOofuLXqNt28G2A9VYuO2QtBy6b+IpG99/ZT5Gf7YWsWQKd7y2AD98YxGO1OrnxWwqyEpJ69+/Pz7++GPs3LkTn3/+Oa688koAwL59+9C+ffu8NtAgf+A/apWlRJWvqygSFjJF0ggJdrhlELklMWWFvEqa3wSVSIqT6oqgo6Sp3FoINuytYpJduxT8EuKQ4kjIFSKIm0QQb6yicOb+hJ0hDvntiFNw27l9Asek8VApH64bEeXGxPehTqxEjOovfiHMWkmrb5dKoKPPiVyoVOPBoygJdukH9sjMg7wlLQhSnIDhZ0kLqhAkBUIfjSLKEkHiS1SQxqRl4e4oilFK2aDYHTPujsziXl9VOJQmF2HbF0x4JtC3pAncHQttSRMUL3Jzyua5mTLrJye/DTpSl0fA1thYo1FRL1DRzQ7K7uha0iBfM4LmstJFIEbT+s6xkJlb+f548N/L8dbcbenrqaKV7I6SJqgUBsaSBvabF/WDjvsrU7dwfvAX/OWyCvu3jBVT5h3B97Mw9QdXiUwJqY4nEUum8Ny0jVi1+wjnzs+1W3C/aLONsaTp8y8BECtUriVNYhX1Iw6h50J6rDDkIgE8J+jq2JyIrCXtkie/xLdfnIst+8VEPXSN09btw+xNB/DSjC1MuypaipL2u9/9Dr/61a/Qt29fnH322Rg2bBiAtFVt6NCheW2gQf7AfzZpVxnJtU5m8uW/2aJwkDxp/u1S0RgDaXdDryVNPQkEIw7xcXcMWWjfSm3tE5eb/pcnDiHCZzhkuQoqH8+mg2gk5AohyVRGOO5Qn3g7d3dHhYVFkCeNd+tUucaRttILZK2HXTJ4m0mb4ilbqcjTC0BdQEsan+jagXez47Rex7i/My55wafbjIChF5MWVJjMxBmJQSuFtqMTkyZOfRHI3dG9xzt+9lXU4Y//XQMAaBUJuf1Cf+sZJc3yKGlAdkpT0Jg0xhpRQEuaDDq5noIiSEyal4Jfw5ImiSViBd1gz0C+Y113Rx2GSV0Eaat7qcLdEQAmrirztCcrdkfPhhj9m1WufWPSAloZ2fIdHKiKabE7yl4Bf9w7jtL/yi1ptDuno0UcInu3NfEUXpm1FU9O3oBv/n22UskUWtLqj9GnquJidkeduCtd4hC3TIjlBiY+TDL2/FhAZe0KWewaQ0ATetHXb6pnU33hy80Y85nYI6U2IBlbU0ZWFPw333wzLrjgApSWlro50gDg8ssvx4033pi3xhnkF/xYFS2clpVR0Mh3URQJMcx/RZGQb2wKkSV1FIUSHyUtZHmDsP0mqFqNeJxM+WolLWRZyrxZMhDhnCcOIZNkOGShXasIjtQmUFmXhZIWDrlCdIJSSkmfZ+vuGLLqY0gcjTxpdmZiDWRJI+6O1Lji31kuxCF+O+5vzNnm/g5qSSuOeIlD+NdGx63JUjHogChFrpImUfQySakDlu9jSaOVwpTtIEF99984tTsmrebiTRx/d0df1N8uEpa+WLfP/V0UCbnWasYdp/62kGWhlWBuSdkOBLqbEro7wxl3R+reArs7ihCEnEEXbvyiRjFJm7Woir4Rzz08u2v9v7k0uxX1omVzApPDi2mz487h2fRdkM0Asp7Ra5FqDmNjnRRKmqQNSuIQTmFL+ro7ZrcxBACPfboWr8zeiuOPEbNk6qSy4NlK5ZY0fyUtTZQSjDiERnUsiVW7M2Qmqvck2uwg40Dm5slaOf2REMYH2/L7HTELOOPyKlHo2Tk4U++mfZU4UBXHuV/r7GkDwCezzpRBy5z0YxRFQkimbDdk4Pvn9kbPjq3ZOE1aeQzIEtnUkDX/W/fu3TF06FDs2bMHu3eng2fPPvtsDBgwIG+NM8gv+OFpCxZO0c4jv3tfFLZ8XenIeR2XO9FuN1OWyN3Rp0xRsmRp+ZZaoQmH1MmN5eWKiUOIix9toatwlTT98ovCIbdsOi8ceYfZsjuSZ6WtZDyilCVNFpOmwzpGCyJVsSQ27auCbTtYvP0wth2olt3u4rIB4pQfQXKsiIShIBT8os0OllxE7aqogitgEIU/IhE0CB11wEUo5SN40/1AC2zXDO6Bey/t77negdjdkYlJ8xHqVDFpNNaVVWYo74XujhZaRbxzSzZKU1O1pMm6UpjrKU+WNJ3HSdk2856zsaS5VoUc2n1M66grBMpSUtDlJySxN1kpaYHcHdOwkPnmRJY07x0+MWlS66HD/U395pRr1orClvP89E1YV8bm7PQD3S+vzN4KwKtoia7VHQb82Hc9PSTzL/uexRsyXgp+8bupiac4SnnxGPrvij24752lnvttd8wLi2f6X6c/hJY0QZ40t0zJcZnLK0MWYouf9fZXF+C7L8/DnyeudfuDvtaSWNLoWHH6d1EkxKQ6IPfLcgfSCmMhN8wKhawsabZt409/+hP++te/oqoqbXps164dfvnLX+K3v/0tQlm49RgUHvz4tB1v0G04ZNUH42YmmHDIYhJBFkU08qQFsOa08lHSwiGvUugn+PA5v1SwLIshdRDVn25DMCXKjUnzWNIygnf7kvQnSAJagwgkRVRM27demOMedxOJZ+nuGA2nLacyqwiQeb/ztx7yHCNQvSOyENKT75Q1ezFlzV5cdNKxmKnJfvibqwdg2c5yJomz4zjaVN18GwiUMWkedkfvuKCvkcUm6oB8c6Q9vMJP4Eel71e+3JLGBnUTQToStoRxqbYjXgiDxCiQpqiFVGDw8R2EbIrEIBuyLKGVXsWGJ4M2cYjA8lLImDS5pURAupKjgJJR6P3LSXLujg78N9Z4yympJ5dWh0MWjmldhEPVcTz6n9XCa+iuovsokbKZDaugCKSk1V9qMZY0+UaTtiVNcs7zjUqUCRUF/85DNXji8/XSuqVtCtCXQSnnAUHqArJpI5FF6OvTbrreevg+llrS4kkpWyFdrkhBA2hFQ1x+YHZHRUyaqAoZFwE9BmSkSPScQ8+Xe46k0xa9PHMLurQtwl0X9WPSHDCWNKo8eg2nmYqLIyFUxjKxZaJ1VRZn6remNEVkpU399re/xXPPPYcxY8Zg6dKlWLJkCf785z/j73//Ox555JF8t9EgT+C/PVGOnUz+q8y5kGUxO+ppNyM9S5qOx52MsY4gHZPGHgu64Ktg+VrS1PFAMpC5gy87Xr/4hkKWGz8mCpL3Q9rd0dsmMmllSxxSHPGOAVHdsnoJdHZ4RZOmroIGpPuYf0oHGeuYn5UWEFvSlDFpvJLmeAUIun9ISVnFpHFKGu86S0AWxeBUxWRXVXw+wljSMu80EgoJFVnHkVjSAgSukLv9FKNfXHGSO8bpsUYo+GUxadnkStMmDhHk/Cmki43sGxPHpOWnLi13x5TjEbB9k1nzShq5N4eGhy3LZQ8WWXx4ywGzA8/EKgWvO5iSlr42ZGU29WTJvdPX69Xz5tzt2HGwRtC2jMs9wCl9tKsdIHV35OnmdREoVi+gex8AxCSxjTrujrKYWm0lLZZiZKOgjIfkEtmlQa3Kagp+b1n890DAuoRmjsuJQ8QfzH+Wl6avtek+p8qj3R3p/HMUeUo4FGISfLsblLSLuYTEJJDbfRNBVkram2++iVdeeQU//elPMWTIEJx22mm455578M9//hNvvPFGnptokC94Y9K8H6Q7kTmZ6+mFAwCKwmH4GQVcS5rPhZbl70ooYl/M544ITXssAlGy/JRJUbmAV1FN0JY0192RKGn6k3Cagt/bbpdZM0sljWZE86Nmp8FbI3V2eHXZ2uQQ76IRd8c2xf7OAoFj0jzEId7FM9+WNCL8W5Kx6lLpB+xOsoD5ubUC9TFp9e+rKCKO47RtWTJrfWGLtEW10XLr2b3RoXXU7Qt6Z5ZxdxQRh2Qh9Ou6ydBd8v6SXdhfGSvo7q2sbCG7Y97cHf3LSdq2x23Orwtl7K65tDocstCptZqZl3Uho5T9HPI0pcsNoKTV/8vkSROMfzK8dBgpAWB/ZQxXPDXDc5x3jac9avgk5HzMVq4IUkbKcfDijM342+T1WVnS/jxxLV77aisAL4EXAZ+PS7QpIMuTxqM6lpTGWGklZCZjXnJp0LlLTBxCvmNR/TJ3R3bDxT3OFTJ64lrhcYKN+yph2460PMbdkVqXayhLmuM4TFoCcj9j7ZN8Hy1GSTt06JAw9mzAgAE4dOiQ4A4xRo8ejbPOOgvt2rVD165dccMNN2D9erX5fPbs2Tj//PPRuXNnlJSUYMCAAXjqqac8133wwQcYOHAgiouLMXDgQHz00UeB677zzjthWRbz37nnnqv9fE0N/Gcj2t0kO/WsgG4xCko0InZ1ohHWtOaELMvfkhayPFaI0RJWn2wQstSugW6OqoBxaTJrI00c0r7eknakNriSlqbg97bJ7fsc3B0B4vogbo9IQOdd8VTrSdJV0vRjx0SwLK8S7DgZ5c+POZRuQztKoVPmSQvzxCFeiyP9XoKko+DBC1MAqyCTw35ui37ly26jBRvHcZCgLGmib9uWCTkBYkvIadWCSqyZ0YiXrjzh5+6YhaCpq2jR7/jX76/AWY9NxV1vLQ5cny5kmxw61oCgyIwx/2sTKcej/PhZAbz5rer/zUExCIUsdPRJnyJTzPg8TUGhUuwdx8Ha0gqPVSPt7kjijFXujrSgrB6borFLxgKZp2RuebbNue7mQcYNUkY8aWPMZ+vw7LRN2COJW+NB5o21pRV4eeYWl5RLJmYkudhDnW9H9m5jSVsaN6bz/ZHrdZhIdSBqJ/HkERVFE8ax5Yi/Bb6dL83cAkA+d9clbOw8XOPpc7d+qjx6bqtmGC7BWNJEfSZzw2wx7o6nnXYannvuOc/x5557DkOGDNEuZ8aMGbj33nsxb948TJkyBclkEldeeSWqq+WEAW3atMF9992HmTNnYu3atXj44Yfx8MMP4+WXX3avmTt3Lm655RbcdtttWL58OW677TZ85zvfwfz58wPX/Y1vfAOlpaXufxMnTtR+vqYGjxu67T1GhAw6h5rXkuY/bFziEB/BNKRhSQtZFrLgXACQFuDP7NPR5xo9S1pQd0dZiRkWRlDujulJJ4g8Eo2EhBYtlzgkyz5zExjbcoFWxDIYRAkhYyvXSdOi/k/gICPgtIqGfC1YZDHo1DYjzKlcEz0xaY5XiKPrzMWSRsYKbRWlxyFJ4UAWxXzHpNHjK2VndtWLIiGhNV0WkxYE5HYtJa3+PdHX0nnSWgnmliDxcQRB86Q1FGTfjzAReM4xafpjjI/tsTUsabI+zsV4E7YsdGytTp/C5Nij+pNYf/dXxrLagVf19yfL9+DqZ2ZlEnbXX2ohs1GqOzfmYhkm3zejTNBKBhyphSJbBGkvbYGvjev1B+m3Go4tWCdPms4mE6BW2mmW4qDW2EzaCcn5oJY0UUyaioLfEXtVyNgdZXO9iBOg37FtAKTzydLujjybaKaMTD/SRCGO4zBKmsiSJvsdJAymqSAr4pDHH38c11xzDaZOnYphw4bBsizMmTMHO3fuDKTETJo0ifn79ddfR9euXbF48WJcdNFFwnuGDh3K5GLr27cvPvzwQ8yaNQt33XUXAODpp5/G8OHDMXLkSADAyJEjMWPGDDz99NN49913A9VdXFyM7t27az9TU4bIkuYVLr3JP0OWxQhrxZGQ74JLFho/udTSoLcPWZa2Vej31w7Eo/9Zk2lHKITxdw9Dv4fk49LPkubGA0mY9VTlqpB2d0x/ghXZWtIEC0+uxCFEIVDFpKmUQx24FPy5KmmW16rrOBlXieJIGGGK9EYEV0lrU4Tt9fEbqkfxxKQJdh/p+y2X3TH4nhhZ2GkFmFXSwqiKJd3+DLoG+TH2RUKWm5bDdjICUDSsiknLcSGsf2ZVTA6ZMyICgbYikXH3Fc0thXV3zK+S9osrTsJTUzdIz8uEDtEz5ipfk6p0ikmkbA99ut99CW4u8HP90kE4ZKFjAHfHBGORtTFn0wF875X5grv8oRKo/71oJ4C0svb0LadLiEPE7/ZITQLvLdrl/p3NpkjGQp/xmhC123a8FPW5IkgZ2WzikXv49UgvT5q4P/l3qepzWrmgWaZ1FCxyiexrWbqzHF+u34tTNZcSleu5SBmTuSXL8qTxz9SpTRH2VdThQFXMU8apx3XA5v3V2LSvCr07tRaWwbg7Uu++hrek1SU990gJTSgtsMVY0i6++GJs2LABN954I8rLy3Ho0CHcdNNNWL16NV5//fWsG3PkyBEAQKdOnbTvWbp0KebMmYOLL77YPTZ37lxceeWVzHVXXXUV5syZw9/uW/eXX36Jrl274qSTTsJPfvIT7Nu3T3Q7ACAWi6GiooL5DwASiUST+I//JmOJBBJJNviXZkuLJ9KKA+0nDwBhy0EqpXZTc+wUEomE73UhCwhB/eGkySH8J7gxN56K/znreEZADoeAVFKdZT6ZTAK2vJ0WHCQSCSY+RweOYyv7YNvBGrQpSn+C5TVxJBIJxOLqtjLtcmyxC0d93ztZCstE/7JtW9p2UVcE6Z5EMt031XX6zytCKpX0WCxtO4XqujTbY1HY8qW+r61fADqWZHbc6UBlHlxIGmzbQYIbYykmMDr9rHD030fISs8bJNaMfE/8OCTunLFEMj3WAr7zRCpdblwy7lIp21U8YvE4Yol61yHLgS0YG8mULQ0aJ/AT15N2ur/iCnevSH3/hECUfcG1jiMck3XxdD/qIt0WvX7VmaeCIBpWl1cbE9PK1wlIHXJVnpP1YyWZ9CeMSKRsJKl3kkymkPIZF2QME6Qcp77vc5gjHBu2z3Oz9OKZ4zV1cbxeH8+UDRLJlHQtHnJ8e/e6RVsPuIKvnUq6868ojYjjOPjJ2IWYvemAsP3StnD1k3vIHgZ5t+lzVCJgm/2ekyk7c10iO+KQGNUOP9DjO6VYo2nEU2T9Y6+XrU8xqp/jiaRwg6OOa2ucG6s0qijmwWpKmUhQfSdDyibXiPv23QU7MHHVXjyxgrWvyMoUWe9StoO6WFz4Pco22XgGTPIcdXF2/glZwB2vLfDcHw5Z6FBCNqRjzHtNUGOPblMtNYdV1GaujycSOFKTUQJjce+4jVP9V0etbWTubwr/6SIrSxoAHHfccXjssceYY8uXL8ebb76J1157LXB5juPgwQcfxAUXXIBBgwb5Xt+zZ0/s378fyWQSo0aNwo9//GP3XFlZGbp168Zc361bN5SVlfHFKOu++uqr8e1vfxt9+vTB1q1b8cgjj+Cyyy7D4sWLUVxc7Cln9OjRePTRRz3HJ0+ejNatW3uONzRsTpydMmUq9tZaAKiEn7U1ACzU1NRg9ldfAYggVlcLJwQQt7I1q1ZiZ7kFlY4/Y/p0HFMMrCxjy/e0KZXCsiWLlddUVhzBnmS5sj4AWLVyBUrKliNihRF3iMteCp999hkshOFIHBCnTJ6M/XWA7HPYsnkzJiY2oq4mDLkToxdrVq/GxIOrsHy/uA86FjnYsHo5gDB2lh3AxIkTsbdW3g4ec7+ahcMHQ+D7ZeGCBShf72DtXnXfy1BVVQnAwtR1cpbFndu3eeqtqqiAqn++0dNGx2IH724Oo2zvXkycOBFLfMaHH2Z8+SXq6tj3sn3HThSV7wAQRuWRQ3CSlrJd+w4cAmCh/MBe95m27toDC5ZwzGzasI5p85EjFZg3bwFzbPWqVe7faTfp3Vh5IMizOpg4cSKqqtPPNn/eXOytZxBPxDPPm6yrBmBhw6YtmJjchFg82Bjdf+CQctxt3LQZcNL998UX07B5V3q8bdu8ETMqNnju2bV7Nyqq1f2dVvzl5w+XV2DixImoqJI/y5ZN6zGxZh1WHEz3aU1d3HPtpk0bUJv0zlPTZ8zA+tbi5xVh4sSJiCX0+vXg/n2e+nLB5vXsWOMxf6F47ly6fIXn+MFDhxFkbPA4UlGFiRMnYlWp/zg+UlmFzVsqQfpi48ZN2F8FqPpm5Zq1mHhkDch7qautw8SJE7G2PPs5Ytu2bfWKl6Rex8H+A/uF52fMmo1EuXd+1cW6DRsxsS4d516bTCtEZINn845Muf+eOhe1tSEAFuZ89RWOHE6fI/MSjUOHDmFTBXfs8BHPdTx4D6d9+9PjORGrA2Bh85atmDhxMwBg/4HMWC/buxfVtZnved/+/Zg4cSKqE8DsLNeX+fMXonIDUR7U3+DiZcvdOrZu3Qadd1FTF8fEiROxp5otv/zQQeH9pXsz3+zyFStQG0u/C77N9LMuXbYMkd1Lhe3fvb/cvf9QZbX7e9fuPZg4kVhAxc+9a+cuTJy4A+sDjvn0+/WWmZTMtf/59DOUlnnHdk1tLQ6naj337KfGYl0s5o4nft2oq4thrSAfYcixsbteZli/cTNaHdzo3negfg0CgN2lmTbtKi1zf69at9H9PW/e/PpvIP33jPr5nB63GzZucs/PnP2VW9fCxUvh7MjvRlo2qKnxMq7KkLWSlm/cd999WLFiBWbPnq11/axZs1BVVYV58+bhN7/5Dfr3749bb73VPc+TNjiOIyVykNV9yy23uL8HDRqEM888E3369MGnn36Km266yVPOyJEj8eCDD7p/V1RUoFevXrjyyivRvn17z/UNiUQigfXvT2WOXXrZZdi8vxpYkwls79C+LfbVVaOkpATDhg0BVi5A65IStGsVRVltmsL4rDOGomLdfuBAqbS+K4dfji5ti1G+YCfe27pWel00GsEFw4bixbWLpNc4Ra3Rp09nzN23S3oNAAw9/XSMOK0Hfr9sOuL17oOtiqIYMeIq/HL+FKmLwtXfuArbD9bgiRVzhedPPqk/RlzWH6/smIc9NRXKNtAYNGgQRpzdC4lle/D2plWe8y/ecQ5sx8E/1y2EVdwGI0ZcgI37qoBlcosvjcsvuwTzJq7DuiMHmOPnDTsXZ/XtiLqluzFuizg3kAqdOnbArmr1c57Y72v4snQbc6xzp2Owo/qI9J5TTj4JPTq0wrubV6Fzl2MxYsTXsXfOdmBr8Fw7BJdeegle3rQQFYnMzlrPnr1wSu8OwKY1OL57VxzYdQQ11fKdq5I27YCqKvTqeTyWHEyP6dYdOsE6Ui50tTp98Kn4eHuGuKZdu3Y48+yTgLVL3GNDBg/Ge1vTbrfHHXccRowYgtDqvXhz43Kt57KsEEaMuApj1swEYnW48ILzMfj4DgCAv62fjUOx9CTfvUtH7KouR68+fTFixAA8vHQaILF0RMOWh9a+/THHYMSIc6Tj7oSvfQ2z9+1AKmnj4ksvxbIvNgH7SnHqwAG4fEgPjFoyk7m+W/ce2Lr1EAB5f4fDYSUlf5u2bTFixPn4S/2zizBk0KkYcW5vtFq/H69tWArbCgOcRf6Uk09GdTyF6aWsNeS88y/AwB7t8cDcydI20BgxYgQenD8FvKOfqD+7d++OlYfl3hZB8fXTh+CDbfJv+NTBpwEbvHPLgIGnAltZcqX2x3QAqvTnLx6tWrfGiBEXYt/c7cA29TfbqlUJ+vbtCpTuAAB8rX8/VOw8AhyRE4t9rf9JGHFpP/e9FBUXY8SIS9Bmw35g7dKs2ty/39fQp1NrzPxkjfgCy0p7zxw57Dl11jnDsHdlKVCqXndk+Fq/fhgx/ET848steO7LzTitZwe8++OzAQBrJm8EdqfH5ddOGoDiAzuAeAwXXHAB5tVuxMaKg2jdtj1QxaYN6NSpE1DBtrVN23ZATZWyLSNGjGD+Hrt7AVBZjg7t2uBgrAYnnHACRlx9MnMOALoc2xW7644A9Tv/HTt1xogRZ+Gbz83B+r3iOnt2LMGuw3KSj6Ff/zouH9AVAHy/wQEDBwGb0/JD7z59gLKdyusBwLHCGDHiKmzcV4W/rMjMacd174qNFQc813fo2Nl9/wNPHYSJezZ65tChX/86sG6Z+/egwUMw4ozjhe2PW1EA6fvtUOZ3xy5dMWLEGQDkz33c8cdjxIjBaL/pILBWn3BoxIgRwjJtRyzzXnrFcEyuXA0cYueqVq1aoX27Ys880a5DZu6IRoswYsSlAICNe9l1IxItAgSW7+KiKE4+sRemlW5Fz959cfagbsCqhQCADh07YsSI9Hcx8cgy4GC6Te07dgbK0++le8/ewJ70d/jcmnC9G3t6vr/gwgtxUrd2eJMat32/9jVg9zYAwJlnnwOsTvflKYOGYMTXjxf2SUOCeNnpoEkoaffffz8++eQTzJw5Ez179tS654QTTgAADB48GHv37sWoUaNcJa179+4eq9m+ffs81rWgdffo0QN9+vTBxo0bheeLi4uFFrZoNIpoVB283BA4uYODv948GL98fyUAIByJIMQx1WWCay3Y9TsRoVAIRRSVdUlxlKHUK4qE0LVdMTMxtyoqQjQa9ZTPI2xZKClWxwzsOFSLi0/y31UqLoogGo2mabfrlbRIKJRuhyITdVFRFMVF8vcTjaTLLYoE2zUk94UF97WKhnBOv2Oxvj53T2Usmb42rP9JtmlVhKigf4uL0uOtleKZVJAlTGbqiHrbKWoLjaJoBEX199lIfxcBck4LEY1EPZsvlmUhWb84lRRFfAlf4oTpjHpPNYog9dbceE05wPNfsopAhCorXD8GRX0mQ8p2EI1GXdcb8k4BoEPrKFAv67YpTh+znXR/qsI9WkXCSKRY4YPcJxt3dIxMOBxx+7VVVPzNLNt1BIdrcnNhJW1SBdq3LmbHuIjYIRqNoEggpFihsGc+jijiFsPhiNAFqljQn+FQKHDSexXatFJ/wymJBUVkAXYkApsuUnb6vViW//yQtAFQ16XvUddvOxbzXhxYWmuICtFIGLee0wfPf7kFZRVihV/mYeFYIVTGsp+gHCvd/qe+2AQAWLS93H0+h5qzYsmM+h+NZuarhGAQieKMdQgp+PFO9hbcOdvK9D39xJbFfhfkncgUNAC4cmB3XHzysUK3N0D8/cmQosYs7wkkQzxlIxqNIhJh57R2km+Jfj4rFBJ+61YozB+QPsOR2sycUMOQiDj+z21lN+aDypeOJfYsKquIYb/AEkZ7e6cc6jm4frEdB0WRkMc9vCgScufqpJP+tjLlZdpPtylOk8Yk2HdClx8Kp+UshlUT9Lihcn3CCtxXhUCQNuTPLyMLOI6D++67Dx9++CGmTZvmKl7ZlBOLZXbShw0bhilTpjDXTJ48Geedd15OdR88eBA7d+5Ejx49smpnU8B1p/WgkhV7hQkSv7O7vBa3vDwPQNrPmI6DKYqEmODRZb8bjs9/zhK9uDmzfBaQUMjyEDGIoMMcSAgv6DxWLomG4vaQJc75xNcdPE+a/BxZiNvWE4cQtqJgedJCwuuzTb5NoKOkCYlDfGK/ouEMAUz+iEMEFPzIxHO0ioR9n4eQjNBxl9XxpNTyzudJ27ivCou3s7vbDHFI/W+//uFhUwx59Pjs2q6V+5vEpGWIQ+Tjp1iU2NnNmSO+z3EyZBhsnrSQMA9f6RGxIMyXqQJ5BNWzkG+RzEuiMsOWhSJBn4vKVX3/MqufbN7KJtWCDH7fcG1cHKcjJD/IkfQhyP1J22audzTu9/ZzHohDLAuRcAg/u/xE6TWy8uMp202Nkg1EzHoENJlBXSKVyZOGDAV/IdkdXeIQAbujzf2W0a/LYFnqHJ1BGAoZdkRBf0YF37eMxl6mpCU4OnhRf97NpdGoqEvgic9ZS7XokemydNY6lzgkDwQtKsRTtrQO0fPTcYnsWGGvdRzxvBgNZ0ic4klbmiCdIQ6hdnCrFTHipA2yVBH0+22OedICWdJELn40ysvLA1V+77334p133sGECRPQrl071/rVoUMHlJSUAEi7EO7evRtjx44FADz//PPo3bu3m6dt9uzZePLJJ3H//fe75T7wwAO46KKL8Je//AXXX389JkyYgKlTpzLujH51V1VVYdSoUfjWt76FHj16YNu2bXjooYfQpUsX3HjjjYGes6mBCF1//nQtvsWZfkU0tSHLYoSFonCI+VAJDTiNTJJM/7bIBJGHrzkF7y7YgduH9XVZ95Rl1ddJTxIZOnr5oqGrpEUD5kkj/XxmHy8RjkshXr/IJOonzaBKGu9uBWSeORvKd0BPyBSV7XdfOJRJdUDW/VzZlkRsegy7Y1TMQkiDkE7Q11XHvIQkBDrKr0XdTX6paP1FSFI5e+j+7tY+Y63PUPCrlS0gTaJCmBoJ/BIU205G4bQdioJfwu6ogz/fOBi/fE/u9kmEQhWDWhGXJ02EUMgSMmqmbFF+yJA855hE2JYpaekxmR8hy48tsk7a5vxT8AfJxZfOk0YLXzrsjuwVpLkVOZALkTVB9cnKnieRzFFJU+SGoxWfmnjKvS4UCk7BnxO7I5UTk4BPZs3ktNJ49yFLvUEZJE8hzfgp2ixpFfVaswn4ywmTsqcOht1RrKTx+GxVGZbuKOfKjyrHi4gIhoeM0bQ4Ip+f6Pt0EU/agWYoWRoGfuylHMflx6ItatFwiFXSbLbPCdg8aTRLpiJnoO0tJyXJjdYc2R0DKWkdOnTwPX/77bdrl/fCCy8AAC655BLm+Ouvv44777wTQDrofseOHe4527YxcuRIbN26FZFIBP369cOYMWNw9913u9ecd955GDduHB5++GE88sgj6NevH8aPH49zzjlHu+5wOIyVK1di7NixKC8vR48ePXDppZdi/PjxaNeunfYzNkWQCfTTlaUeq4BI+LYsLk9aRGzBoaErwKnypPXq1Bpf/PISAMCfJ8rj2tw6LeKKlbEY6ORrs3zaS8oV7cqrQOSrXp1aY/qvLkFpea1L50wE/SJ3kQSem7YJg3uqvzEaReGQMJEpeeasLWkazykSfv2Uwkg4kwCZtDsvyay5Y47joK6+3OJI2Pd5RJa04kgYsrgqrbFNXeK+j4BKje1kBAZaWO/WPmNJa1OcHutJjRxWoVA6cTy92JP7pHl5HMf9dmzHcXe20wntgytpY24ajG99vadSSXMXXYUlgihI6vyGEErnSYEgphojst1X2byVTxZ+v7Jkgp9o8yZXS1rKdvD89E144nP/GNJEymZ2s2X5l/h7aNiOg7fnbcfDH3tj7nRB5m7VBolUSUs5qMxBSbNtx5MigcTG08JtbSJF5UmjKfj15sZcLGmuJVpSnu3wedL8y06nRZEP3CDtjafEwjxBSTTsJqvm4bWk+StptuNoKZFHBC7dbYsjWkqa6jsgp/gtjXatoogJaO0JRN+7ComUHWg+4BVZ/jdx8abXrJJo2FWKiiglLZGyPdZLAjZPWqZOms2Uh9CSRpXDp9VobgikpOVCry+Cjvb/xhtvMH/ff//9jNVMhptvvhk333xz1nWXlJTg888/962nOYIW+HZzAb4iAdSyLMbVrygS8nVByeRfUV+nypNGC2A6+YeIiyVjSXPztSmUNEsteBMhNXAya6rOE7q0YXZxSFl0mX+dssF1X9NBUURsSSPPEtS9jr9fBbElTd0/EcqSRpqds7sjvAKBA9aSppvMOhwK4fU7z8JjE9fiyW+fhm+/KCZwUVllCZjxVv8zqOWJ3tWlXTZpS1pJ/Xghu90qAYhYrWNJ74Ir+04dx6Hy5vnnSSMQkWoA6Tw6ADwWPRqu66bKKhjxfj88QiFL6M+fsh3PDrCqHJkAVCyINRXlzMsGQ3p2wD/+5wysLa1UXlcrUdLozZtTerTH2tKKnC1ph6rjWgoakN75PkLRZqddd9X38AKU4yAnBQ3I6OhqS5q8Pbla0vixk0g5KIpYzAZEbTzlCpqWlRmLhbSkkfFB1oixc7ejT+c2+NEFJzCCMt9+LXdHqC1p2eZJE1m0WwlcuAHimcIea1ssU9JYy5BO80SbIyU+azeZd1XvS5bMul2riDD3GIFos1aFeNLbPyrQfS/yxCDrim1n+rNVNIQj9eJlJJxxPY8nbVbpYyzumbJ1ZQNXSaPKoacSWtFvjpa0Ro1JM2gc0Fal+VtZti2Rj3coC0saqcJPGQ5Z8lgvup06+pHIkpbZSVUpaT7ujvWn/JJu8+CLpOuQCZkqsz5fdjhkCXeGco9Jy05J87svHMr0M1nsc500RfqS4zjuBJ9OZu1DHJIiShpw6YCumPrgxTi91zGMyyINHWXLYn5npzTTFh/6EeiYtDb17o4ZS5q8PLr/6TrS94lvtJ3MZkPK5twdFcqqTHgiyquqJ0ibtNwdFWO8LpESnk+KlDRVTJpk95X3QiAI4nok68LTeh6Dnh1b+1IlxCTMO0SoOrlbO5zSvV19u7SblRds2pchl3CgY0ljz+cjLicTkyzuSceRj/14ru6OVAynW2bKxl1jF2H8ogxLYS0Vk0bmdUAspIrmpGyUb3IL/X388b9rPOXxY1+nLsuylJ4rQYwZcR8rSIlknqlNpDzvta00Jo2qI6nXl6LNEb8NVmIZkrlPA3Q8HXtcpmASBLWkxZLBLGmyZNZkPBAZjn5f9Lth3B1Tqpg0sbujCuJk1uJxEw/YT00BRklrgVDJmDoxaWnCCnUdRLDz+yRCnJWOaQu1sOpYL8jiRlvSwj6LtE75YYHVSwd8nbQSk7GkZWftIveLJvyMkpZfS9qNQzPxiyJ3Rz/lhSYOIUKyjp++ChZ8iEM0LGkEnrEvuU0lgGSuoYpxLWnq8fPOT85xLU1AetERWdI6Ute0pixpfrvcfFJ6wD8mzYHDxKRl3B1DUD2OTHjSCcsTua/wIFYsleJbFRMraSnbm3A7nIW7oywmLYjQLJtT3HnLp79kxCEvzdwCIP3OXbKeBtbSNlJKmu1kZ0nLFWQOVpEHyd7XkdpEVlYqulz+mXYfrsXkNXuZYzXxJPWsGW8DUd2iyD6dZNZj525jlF4idAu/D4mrGKBnBbMsH0ua7WD7wWqsK/OnIeeTKPOQWa9q4ylPT8mUqGzc4URKmmzOIyBJs3kXWBoZSxrv7qhW0nTGAI27xi7CrI1yF0IeCUlcImmvaB5uxStp9YyVMYUljX5sXUsa6Sv6e6F/02OoObo7GiWtBUKlsIhj0jh3x3Aob+xDIYW7Iy3004Lx/Zf1l5YFsG5IpAw/HU+1iBMFLri7I1cObUkLk3ZZWSlT5H2IJp0McUimvSd2batdtqgvRt80GD88/wT3b1Gb/fo4HApRxCFEScvVkmZ5lTQHjCVN14Klq8zphaRZ1G+98od9rTMW/vYK9+8UFR9BvxLa3bFN/Q5rwvYnnUkz3cksaeJ7bAeU9TOz0+xnSZMJTzpxbITYQ62ksTGdItTEksJxmkx53dCiiu8/iLsjEIx+X9Z+MlZk1lyCOp/d5nDIct9Tru6OQUEPR8fx37DzKGl5aEPGk0J+jcyyoXIv00HK9grke8q9+cNqExmrBu+1olePf0/9bsJqfLBkd+aeFNn88dZFb/Z4yVz86wpZ6u885Ti4+Ikv8Y2nZ/mWxShQgudsJbFmV8eSnrbKvjWGWEJTiBetW218rF1kTVIpCqTFQS1pQb/tg9Vemn0VeHZHXjESyUb0GkDHpKWJQyiFiraqUe8sKLspE9smsQYbd0eDZgHVBCqyhvALR7GGuyMBfZlop8my5G6EdFtogfCXV56Mbw7xpkEgbaTdkEgZfs1V7ViT+Sc4cQh7vcjdEcjOLZHcL9ptJQot/c7O6N1RuqDxkI0B5n0IOszXkhayMnTuTp4saZZXkLUdx3WVKI4EsaSx18nu0rHq0peEXEFRfh8RbGiXxGQqEx9B19mjQwn+31UnY+TVA9zd4aq6hK/LS8iyPAp4ZmGTWNIcx20/w+4YCSk3e1pJFBid2FIdhjUy/lUCbXU8KbGkOZ4YDlU5soVdZEnzU6p4yDZowu4mjvp+v+8nRLmeBaE+zzdsBdMhQVUsiXv+laE6z5XoBPB3dwTk8TwHBPmigiBl256xs+uwl6W4Np5JlGYJvlEaovGla+2bu/lgpm2OXLimi8vK3RGWsr9VdOo8WEuavrsjzZhJIJMz6HkzFyHeLyYtnrLTc4/S3VE8H8vSBxCIFNh8gh9jrlumSkmjLWkRnoJf7D6ZzTdPmsa46TJKmtwy3BxglLQWCfmHINxZc9iPsCgS0t7lpK+b9quLPedV9PcySxognhRElrTM86hbrFoYSbn5jEnj3UeDIuPuKLek0TuHluU/0Yvambnf4trvvcZPeaEVEDKhErcRv7598ftfFx4X1Zh2d0z3S6toWJuwgx/7ssfRcXekFXTyU2UxFW1I0AsKPz7vvbQ/7r64nzsOluwox4hn1TvTIVFMWkody2bbmQ0Mm4r1i4ZDyn5oJRFYyLP5sb/5CZ4Zdkf5uKmOpYTzWcrxCkqqcmSxEbKYtCCQffuuJc3nm6r1sUSnN1fSvxtRR6vffVdfM2vjAUxcWcbckyvIJ6eaA2RjrSoWPB5txODu+NYZPQGkyZH4jZMdhwRKGpMnLXjqFF0rysHqjGWQz5MmK493OdN5J37ujodr9JVfNiZN392xJp7yjHe5khbckiZCax93RyA9l6gUBdJmL+mJuuyg7o5BIaLap4/zfctvvkfDISbdkJzdMXjbiGLLuOlKaPdzeb+NBaOktUCoJnVRrFEyZQeOSSOgd7NEu2uqyZy+nl9khUqCICaNCJK5WdK87o73XtoPPzi/r3JB5Z83r0pahEx4AksaicPglLRX7zgT/Y5tw7gtiiASaMKckiYSav2UF5oRkIxBYglQuXOc3K0dLj7pWOE5kbsjnIzyVxINa/evKi6JqVPjGlFXqGLS6LFCLqMFJNmt9DvZeqBa2aZwSBGTJvmg0zFpXksa+f5kwq9MYNGRP2sTKZQLKK5pFGm4O1464FhhHjVRrJBKgZbFRgRNbi+CPCYtfdyvv/wsaZZFuTs2NHMIhXRMWrD6/TOr+YOMT6WSJrFs6JI40Ti5W3sMqU+jUl4TxxtztjHnhUoaxe4YsizteYhA15J2kLIMZvIvei3rjMsZb0nTiklTW9IOVesrvyy7o/c7lFnsq+NJz5wm+9bo/uOJXoJAh5k5lrCVigKRl/hX6hfPHJQ4JCh4mZGPG+bnz2goxIytaDjkymXxlDxPWjbWfhFxCBOTZtwdDZoblEqaaOfZdpiPME3BH/xjEs3bqsmcVQr8LWnkGjpgVTeptjImzXUfzFzzg/NPwO+vPVXpjsR3Jf0Movi0ICDP37ltkeec216qDsuyMKTnMfjil5fgilO6KssWjYFQSN5+91gOljTVAhcKCRSxelgQ5EmD4wqvJUX6SZc9ljSJOqazG2fBQt/OrQEA1512nNtWGUQKML2gyMZnkHhGkdVaKyaNouAnyg1ZcGXzQC4xaQBw8RPTleeJgiRzU3zt9jNw/WnHC2PNkimvpU60OUUgU9JkMWlB4Jd+xDeZta+7I5qIu2Nwy1g+mhvSUdIk7o7ZKGl0OpdZGw/g3QU7mPM7Dgli0uJUnjRLHR+ZCw5RcUguK1+E7ZdEvUse/TcNPXdH9bg9HCAeKi6xuBDIvp9agSVNRvTD1pe9EC/zHqBRp2tJ4yQWvyUsKAV/riDjVURsBaTnZXptioYtlzgknmRzKIqISILA5qx6AM/YaYhDDJoZ/Gi6eSRtxxViCENcNh+TaOJWySCqPGmd23iVk4y7Y8hzjBYmp/1S5HYpb4eoT0gd6hgU9lxI8jyi3X4/ECH1b985HWf26Yg7hvXxtJdWZOmW+AnK4pg0f3dHP9kiEvaSGNTVC0KESl6EkCVf9EOWIE+ak2G9axUNByAE4ZQ0yW06ZCftSyKY+MCFmPyLi3Be/y4A1LvQtIJLmku7L8r6VrW5wEOUaiKTJ03cNttx3H5I2Q6TJy19XlyXlN1R0KejbxrsOebH7FUclbM73jcwhQtP7IJQSEzKI7KkqcZITKII5cfdUVyvS3jkc79OTFpGyW5Ef0cEt6TlgznEJQ5RxaRJrBBBYqcI+NhdHjsFlrQaji4+aD5FXdBEKDLhmk9yzPeNVp40Szxftav3lgji7kgL2CKLoayvqmNJ7Zg0pr4chHgd5bouYStj0mR50vzGhKrMQuCTZXvSCj2JbeT6NhKymI0vhoKfo//P1d2RtIEem3HJuDGWNINmAdWui2gxS1vSMu5FlmVBd+OGnmzECZD13AX5y35y0ddwXr/OwrJo4YkIcfS3/7VjvUyHSjIVIuRQH7ssDxQNtSUtczwX4pD+Xdvi/Z+eh0sHZKxjfhT8fsYM0WLGC/giy4Pfrn8kRLk7EuKQ+kmzjcLnPhyy5Eq04LjtOIy7o64i47WkieEnGP/POb1xQf8uaF0UwUnd2mXapViBaBcn0Tche4YgTHDphdNrSdt1uEZqNeh3bNsMuyOVnFc1ZsOCeuhzPK4//Tit9tMgmxS8YHRi1zY4sUOmn4Xu24LgfWW+tQDEITw6CTaTdOrVjUnz2zAIhTLucw3N7kjjcHUC68rUibl55IU4RIO0RyaY61KA06DdS0WoEih+NBttKGQpNww0DdFCiJLY899pIuUoLWk6QygkcXckqUOyjUkTKSKyvhLGpGmss9kK8Wef0Ilh3ZWhLpHSstbxY99vHmhoC9GvP1iBl2ducdc03hsoGg4xuSdpJe1gdRxPfL7ePUePt2w8tEQxaTJ3x0K7hRYCRklrgVAt1iJ/+ETKdj84MtHpxgscd0yJ+zuoYM+QKXD3tmsVxTs/OZfJH0Iuof3UM5Y0reYKEaKEVIKMG6W8YFVMmiw+jUC0oNx6dm/pPbQQ71LwU9fQbfdTpkSuh2GLFR6CKtzp9mQUvVgihbpEyrV4qeiLLYG1LHNO4O7ogHJ3DGvHePBjTIa2ipw1ndoU4bEbBwvbS38LnroV8ZeA3NIbRMEPS5jjLvjLdPx47CLm2K+/cTLuu7Q/fnTBCZmYNIpSXLUr3SoSkiq4on7RYXxky8hsQPBkKHxZou8oZduB2B1lljSdnXk/K65fnjS/rhHla6IRsjJtaEwlbdLqMv+LOOSjtTrujrJ+yZZ5NhdLmAW1622ucAVaCSsf7+4oI4xQwYJ4vsooadnFpCUEO8OyvqqJe5NZ63yv2SQ7blccwfi7ztUqP5a0lXFvso0JvyGRSz6/bDFpVZmUgj8SZjfqaOIQHrla0siwYGn9xe6OhjjEoFlA9UFHBbNrynZcczaZiHQ/pqsHdcd9l/bHa3eeKc3BJgO92HUoETMT0mW2rneZYyxpLnFI9pMYHZNDYGkof7w8TAvi9HOLYtLaC573yoHd3N/8hCcSVOlr6F1IP8GvtcD1kHfjkblEqhAJWe5iU1GXxFl/muoKmd8+s5f0vrS7o/ic5f4vAwcZd8eSAO6OXnZH8X0Xn3gs7jyvL358wQmec6oFuE1xBHNHXiY8J3OFBdJ9LWtLEIEw7LNLT+Psvp3wq6tORqto2G0PzXKoUg5LisLS9ubDk4tY8zNtob4r7tpoxFth0vbmSVO6O+YQk+bX33550nKPSctYdgpN000QlJ1QhnxY0kj3qkiNZP1C5qYg35hl5aakhSz9b1SEZ28dqjxPLHkZ4hC2rnjSZtY0D2GExhgKSearTq3Ta1qgmLRktpY0QZ40HSXNJ++gCOR5dcqvS6SU8pdLbU+1feb/u9R3HmiMWKvKugSe/WIjAPGmMa1AF4Xl/ZM7Bb/X3XH5riPub0McYtDsoPoORCxCSdvJ0Lr7EAbwCIUs/Oqqk3HZgG5iQor6Q4/fPATDB3ZjXAboxW7EoO745pAe+P21A5n76UWEWDjYZNakvVrNFYJYYnSemc5FxseryfK+iQTeDiVeRYmep0U7V3w9tNWE3gH1EwHE+ezUFgvZMaaNoRAzviop159LTz4Wj904SHhfWGlJ80YFOpy7Iy80iXLsAXp50sb+8GyEQhZGXXcqbjzjeM/5mM9i2aNDibB/6fHAL+Iq9ylRHIRMcKGJW/zApr9I/0u71qlch1SuwEHjUkXgF3u6D/hxIrIcinIVqcauTBES5R3kq/OzioiUSCBj1fXrGy0lrb5RhabpJpDFIwZFPkLoXKbbLCxpZKNHh7WPrk8nRYcMlqW26qqE2AHd2+HrfToqy69L2Phk+R73b3580nFGsvp11kGVu2MQqw+tfIjekzwmLeUZPzpeB9m4w4liwHmQ+dLP3ZGPSbvwxC7o3bm1sD8HH98ep/RoD6DhY9IAYNvBGpet1MPuGLY87o7FYfF3lKslbdWeCjz88UrpuGJIRIwlzaC5Q5Y3pYizpGWzgKqsL985sxf+efuZDIEELZxGwiE8970z8AOOPp4WHMm9rZhk1ul/c5nCVIH3/JGOrTMxKPy8SguQfu6Ox7SWE6MAXsGBVaBIuWJLml9fiJj5Qha7Gy0SHv3WQJo4hEeraBiDjusgPKcSoC14BfNY0nYn+1ZFYY8iM+q6U8Xt8xGuHrtxEC6iUgGISGN0FgEhMyZ1jN/tUylWou9VxoYZUsSK8aDHJHlntCVNtWusEtKzscDy4Mc+/Ux8UUXMbm76d8p2PK5TqjbILGm8Ati9fSuMvPoUadtE8ItJ85P3/YRKWuhvKI8oHZa7hkLY7cfgShp57ypSIx4W9JLdq+5XzUOqWPA09b26/LpECj97d6n7N+/Fwcek8dDJYWhJPB/aKVzaM+1hvwcmJk3k7ih54NqE15JWHPFn+s3G0uLmUaXazj8rWVP93R3T/2bYPuXjN2RliJEamt2Rh5fdkd2QjUZCCktaZgM8G4+nZ7/YiLfn7ZCejyflMZbNAUZJM2AgZne03Y8ww+om/5hka5Q4HkV+rw7fAz2JZ/Kk0RT8wSx/ImSIE/yvpZUrlWDgx+7YsbXX3ZG+hxccenRo5f4m/Uz3d5B4FGFMWohVsERP5reDHAlZwncaCVlMDjVvufIyRX1Mk2CURDMxaf9zTm8seWS41HXWU7/nT/aAqF06w0z0mHRZQZQ0WVJxUb+k3U31pvwwo5Cnf5MNEd71lYeMfj9dVv2/9DGtFmXAu+MyLKZcYbSSFHEFGoElTdEtMiWN7oI2RWHMHXkZenVqzdafY0xa8N7xlhNUCc4V0ZCYVZNg9E2Dcd1px+H/vjGg4G1x2R1zsG619kkkTCNXd0fLUn+jKiuXBf8ND34siyxporU9Qq2BfoKujDhEx8LIC/K+7o6S76c6JiYO8fsesxHiSRPodbxLO5ZEhKypfu6OZAeVvAPSWtFjRij39cZ24+O9NCLcHBANqd1B3XydBWCgpd9pQ8+F+YD+FpFBi4BoEkumMnnSilwlTV5GkJ1EXnGjP6Ig9OI0aEuam8w6q5LYNulMIMdQCoCaFCXzWxSTJiLSoF8NLzj06FCCl277OkOkQoPeafN7DHFMGjsJiyZcvwUwwiW4JCCWFxXNvgwi4hCipBHlj7SrQ0kUndoUSRV2DwW/tFZyPrsJ3y/HHO8OoxL6RAIdieHjHeHS8S56bYwK3GeJa52f21Bwd8dg/chb6uj28OXT52gCDd71T9WGmCROhY8jFJXhN4dJKfjdHfTMsW7ti7G3Iia8XoZcY5yygWVZaBUNI5ESU9gXR0J49tah+GLt3oK3RYc4xA9B3R1zU9LU1tfF2w/L6w4Fd4/lxwZPHEIQDYeQtFMMw6sMFsQKmY5cUBQJAdQQj/u4qsn6WhSTFqpfD1SsndkQS5DnKqbmmi5ti7D1QLX7t2tJ82Fjdd0dSZvJppZE6eU30BoLhIGZjK40UQhlSVNswgLpzYcICkNuRBTY+y/rj19eeXLeyy80jCXNgIEsT5pLwU/cHRVlBNmt4KsTxcIEBS3EEYuUzgbNpz+7AC9+/+ue46RNJJapN7Vbzgv8x1AWMFU3MJY0gdArUtLoiVrkgnPVqd1xXr8uwvqCrD0iocSy0sI3Yfzr0cHLVOi3CEfCYksayXklm8T9hB6+2tp4WjgkykK/+pQLfbu0qb9eTxn0Ux6y3ZQLususdHcUuQ9KAvfpnVc/0MofuYUk5vWjsm4VDUvVV7HbjlaTXCjdHblrGVIRK2NJ4wU1pbujRABi+lhyu1+ycT9LGt2uH1/wNbz5w7OV5fHws3oWCipFnbgMN0TatrxY0gK4O6bryroqX3dH9b2W78YRv+HAj7845SpOg6z7og0OTzss8Tet8w747yXbPGk18ZRwM87P/TgbSxqZu+mNy46ti5j1obXr7phSWr1szu3PUozfsJXpL9lGUkMhvTFIbbCHLRxLWRP9csFm8nXKr9EhZhGBvNNsN/0bG8aSZsBAJjQQCw35V+U+GER4VVoJspSCBx3fAdcM6YFu7VrhgStOBKCXMuDU4zrgVEFcFGnj1/t0wtQHL2IUFL7UYxQxaTRooVzUB239LGkBBYcgPutCQoT6h/nVVemdqINV3h19HXdH0TstKUrXJxNuVMpS2pLGnq+hElkDwO3D+uDyU7rieAUFvqp+ui7mb/XlUoj6SSWYqd0dxe9KdEc4ZGnvyokSyW/cVwXAf7EsUSR5FvVxYEtakdySxpcl6p+UbaOyjqUBV8luMgGIvkWm5PkSh0jOkzHCun8Ht4pZln4cYj4hi0v8x/+cgS5t08JbUB2tS9tiJiGzDohclkucWJsAljQ/d0Ue0bDFWKZClpU1BT+xoKvAW1z4sSGzJNFhDn4xabm4O/KMqWxMWrreKwd2w+Q1e3HJycdK32u1IE8a4C+oZ+M2KCIOaV0URpuiiMumSdbrFbuOuAQqIpAmE/GKdJlY6c14pmST0y+fqE2k2I3nUAhfO7ZN5m+fMa3j7lgUDmX1flwlrRHmwXygeaqWBgWDTCC84MQu+OXwk9w4AtXHpBIqvYJudsKpCq2iYTz/vTPwu2sHuvFHObE7Uu3o37UdY+Xiy6UtaUp3Rx+hgbcW9O3cmlnkVMmfRQjiRqDD3Ci6xt+Slq27o7xMC16SDKKkEeXPsiz07NhawzIWXADOBqJ+ynasiBYeWRxSKEBMGl2uyoVQhLbFbMyfKL5NhdvO7aM8z1tpIkz57LWitiZtBxV1rCteNpY0+h7Z7aI57NU7zlS2L122t46wFXxjgN/hbijIlDTaHTto/EmXturE4CLoJLP2Q5ANMQvBFEK+nywrhxQGEuWIBr/hwF8vc5sroshndKxNomZouztSoBVYsoad1usYLHlkOF674yzpd1cd87o7Av6W7WzYHV3iEKrtJUURZo1uXz/u31u8Cy/P3CItyyUOcY94LeoE4VBm/sg2p58KNw71shfLUBNPMet0JGzha/UeLID/vEX2kFViSraWNJL7rjE8CvIBo6QZMJBNpMWRMO6//EQMOj5taVIZZoIInPyl9Lwa5KMq9vmAc4tJ07+WJvxQ5ZShn010FW1Ju+/S/vjsgYtys6TR7I4+wpFYwNe5Rt1RYQlxSCs/d0fFeBLdQtPvB4Hf2OSrylb29WN3DHJORMEv21EP4u5IK9N8/X4JlPt3bcv8TX+bOgqDLK5SVB7ALt586SKhLJVyUMkpaeqYNH/iENlzierv1j5D8iOKRwXEYz4cDgXW0oLkxssnZAyPtFJ6Urd2gco8liNj0AEZuw0XkxbMTZ+fx/lUJ0Fgwf/74pUwviqZsB+l3B39GUW9yuLTt5yu9VwqV2qipFkW0KlNkdStGyBKmve4XxtUzIsykCLpsZ3Oz5n5W5T3VAiHtSiRx5NtnJKcsDRZVr7wf98YgFvPlucvpVEdSzLrfyQcQg9qnttbUae8n3j6+FnSsgHJfdcY82A+YJQ0Awa6gmc27I6AV5BXk2sE2JH0W0hz0NKCEETQ7o6qnFl+iyltrevfta0nQXAQwQFQs4LptM0TryWYOfzeVyQkFkBa+VjS1O6O3oWaLOZBlTReuPJ769lO+SIhLlslLSwQ8qXuRho77QS0csG7QB2pTfCXMxjQnRW+ixglLf2vqhltfZU0uSVNFVdIfqYcx+PuqBq6vOA6+PgOmP/Q5awrouR+keWYtlLKLGki6u2wJY85uvO8vgC81qZchP5sYVlyl1f6eU/o0gbv/+8wTPr5hVrlitKS+CEfMWmi+GAZLEueZkQEfh63LH9rjwwhC76TUl0i9f/bO+84K6rz/39mbtleWGAbu7DLUpelLyBFijRZGxZEVJTEhgKCGI0ofoMxipqomGg05puvRhOj+SVYYowBG8UuCqKQ2EAQKTZ62907vz/uzr1nZs7MnJk7t+0+79eLF3tnzpxz5syZM+c5TzmRftuvosDQn8wWJNgJsn10R22/feXaMZg6sJOYkCagLWHzNsvy4LEm7mJkISdqMovdPpdWZGg0adqQ83am9ir6EPzq/fHazi9LyGwZCw8d4wfpUXGzoOhECx/WpDHjmqzdL9Dum9Ec8cUzT+PeJy2cKQlpRKtA9GNm9eGyysMqUAig9R1zYqZjNyEX8UnzgnxmgmkVycmumdmPtzrYWe2TZgfr7G3XErxmFzF3tLsnv48/YcyyDRxinqdVkVbBC3jo7+mWM/iba0fKTpS5o9PAIRJ/nzSfLB7UgC3TbgKgp6dOSHOsSbOZFGfoBAC/xifN/Dp1AtDs1NxRN3EdUlWEkvxM3fPnX897PqzgZuZQr7Y/W4TPJHqfLAHXTOyB/9x6MibWlurOJV5IA8yFT70AUl9VhO7FYho1N5Ms3tjpFCdCkyQ5833RLy5K4EdsFSvbfp+0Y02hyPjz8Mx6Q38y1aQxz9MuQmFYoxf9LarNFDX1ZFOYPdeDx5q4Jv73njsAFe3MBSY3gUPUUlghIjvoxwXDOqN7cS4ev2QoigW1wProjlLE3NGYVpaliP/4IQtNmuvX34GQdvBYkzYEf8s7cGNDLxTnZeDy0V0tr4+aO5rPTtwuXqjP1BdLRJ8kkp61JuKG6Et559n90L04F/edN8BRHvEyd7QT0mKJ7Ork+57B1MMq4pLm3jh1Y80dec7DTlZ3AS980qx/m13H4pdlroCifmhM90mz1KQBN58S3kCY3SsOsNauvnPjeCxoCSqjoi//9P7lWP8/EzVlaeul/d0xLwMPzzRGB9XDDU/tYeAQn24VM3rcfjNXXr4HGSFtXM+OuPPsvpbXlhVkamZSGk2aSPAAm3fZYO5oETiER1OIo0mz+BIa/XjU/+1X9HkT9oCAJo1fBl+PNrymPQqyAsgM+AxCuCyJj6Neua5JEvDtwePcc2b9VaSObuqn5hvLKrqdDyYLz9TPCv1imyxJCLisq4jm42hjc2QiHA7ZLxnO82AXE443W5vW6c0Q1TrZfx/445YhfwFfUEXhLy51L8nDy9eOMc3bzuCE249armH7SdAn49ITu2LlwjE4sXtHW+sAffkK84wA/rjml6XIQuRhi4U0WZJc+eQ7WeA5dKxJ55MWrvjlo2vw9o3jI/5pg7u0417frDPz5BH0O1t0VYlGd0xPTRpFdyQ0iPbjbsW5WLmQP9hZ5WFn7si+oo4csB1qluJFpp8V0iw0aTYNzQphPs5HLhZzR7sBmzdhFTFTtQsY45P5HwvVL8PdPmkSRnTrgE0/n4yXNu/B1X/5IHLOSnAvzs80fDB45ViZWOmnzG/ccJLQhI7rb+RgYUNzjqdJk80dzRVF7J0y06Q98iPzEPBDq4owqnsHw4SCNU8UGV/s/EsN5o4WIfhZ1IkPzyfNqo8Z/Hgi2pnoMbPLeZH6/JrJnEmf52rS+D44kxjtGU/jLRp6Oivg88y35eu9R7jHzVbD/bJku5DkZl/CiIAQwwTNSbTFsCbMiZCm90lzb5opwV5A/GDb3siCpczRvJl9szIcatI0psARTRpzTDIunPpkMVNRSfPe8b9DPG155HwMqxF+n2SIbsnTpOkXZ/TBlMwI6cz+1D7PHeclKfKNs9akSXDj72FmkcHj0PHmSJA2AJqFBvYZ/eWyE9Bj8b8M1zc3q0KaeRluzR3VPp2ugUNISGvDSJJxwh7LxyySh8WbbZxEaM+zduRO6mJr7piITXmgDV9f3SHHNB0bYIRnisnug6YOcmzTOdWkNTmIWiXkk8Z5NCJmebyPqmoiaqpJE+gH2UG/YQpn1yf0H2unJlH65KIffy8Dh/Aw07j4ZVlYo8quOuoFGjP+Ons497hTc0e9EGaVH6APwW+bfTi6o42PBItek6YWofVJMxYsSeBqRdi2NRMAeJtZh4U0Y9qS/KgpFe89FZUxsoPeCWlmPihmixhBm02GAXdmW1Y+PaI40Ww5FbIMPmlwH4Jfkuz7/3Mbvo787eNMwo+ZatKiCY/a7cklSVCYR8nTCudm+A1ClF9Q06/RpHHO5wR92H+0SfOOW70jTgjIMo5C20/VuQW7AKHX2vC21OGhTlNCUSkNAL/v+xhzx8PHzcdot7crS+LftOOMGS1gPq4F/TI6FWZhh24RJ+qTZhU4xN2NqJo0t+aSyYbMHdswTn1jRHHiXxNrearD/HWTrXeSj8NG9lwyAz48P28U7jtvAIZUFRnO33FWX0zoXYyLhldFjunHJUnSagZ5G9s69kljGsCuyXkfBP0h/qTUSkgzH2rUyFdmHwTROY++TnYmJkbtoHX+Viv5kiS+qOBUC+l0ghmefPHfbSufQBb2+lj34NFEXxS4Fd4+fSx6n7SAxXYBLOo9hfdJ005qrIYHw/1zJp1mCxs8/yKN74aNia/ebMxM+OblDYT7gqiPk1MfTiuuHFsDAGiv2xPK1FdNYJVckoDfzBiI0vxM9C7LF6qHJ+aODlbwJTjzATT4pEkxbGYtIKSx8AIJHTV51534pMmS9h3NzwxEylPhCS2qBswOrSbNeD6vpbz9LSbNWQEf/jV/tOU1ovDMlyOaNKaN9N8Cu4i10by0mjQrU1EfY+548JidJs05kiTZ6t/6dipA0CfjdzMHa76fVgIRrzrqc7d6/rEGDnHr65lsSJPWhvHJRtW9FxphS3NH3Rs6tlex5rdTWepnp9Vi4aQekQ9Bssnwy+jaMTeyVYGe84Z2xnlDO2uOGbSZkqQRwtSxn7XXznEYgp8d/OyUirwJvt5W3OnAbzXxUD9gZmOo6GqePll3XSh4Q776Ca1TjRWT3okJDa8cq7D2TuslSWYrr9CscKvUdcrHu1t/cFSGE9jJi0g72WnS9KGY2Y+vSEvxfNKsMExK1UVum3uRJf5kRRMFzXQz65YymGNmmrQAJ3omW5bopN/pwo8ZEiRcO7EHJtaW4LM9B3H93z6MnDMLqiGyyi1LEk7rX47T+pfjxqc3YvPO/ULXsP+7wYnQ5ET7APCjO7rV+jmJ3grwx1sznzT2nTPb8FpFQlh4ePqqEQgpUasPNihHbqYf2Ke9zi9LGF7THu99+T1kSTJdHMrR+Gsb71cVAFVN3ajuHVDELBa4DfgE8Cf6Cmc80PcBUU2afp80NRczIS1DyCdNqGjudey8YXp9JV77ZA92749uKD99SCWmD6lEwCfj1uc3RY5bLcry+nd0M2vz+jjxDeXlna4+aekpWhKeYGbnHCtWgyD7nj104SCcrxNYnEppkiTFXUArEN3jBN6sRsuSVruhfgRY35jsBG9mbRTSjNdZ9RyriGfqyqfZ5ET0o6pP1bPUerVdX5zjzayZv51MrHhJmywmPnZ599UtCJhtZu3TBW65/cy+OKVfGa4a2y1yrLwgE6/9ZKxleSKwOp+gQ3NHe02aPtiCpmBT1PDbx5tClj4ceszMu9iieHMSSeL7YLLXmb0XPOEi3A+M6VlzPIN2WBbX8DrdsoJl6oByzW+/T8agzu0MArXZXkcifnOa7RQE6+XFPmkBn2wa8ECPE406YPRJkyXJ9WQ0vDjjZBwyjhNmWjJ2IcDWJ60ly4Gd22najRW6ePt8+mQJV4/vjk0/PxmLT601zZ/dL4/X1KoFhWru6OXcnLeYwHNX0Pc30cAhIUXBF98cxG9e+RSAtbku65NmZabsdk4nS1Jk/zIAuHVqnUGbleGXI/2VLcfqe8+rT1RI836fNJV09UkjIa0N49Q3RhSr7y07STy5rowTgj+1+MXUOlQWZQund6rhAoyDvCxJGjOY4y0ftyPMQOx0wNJo0mxambcSrJcheAKN1bfAytSAZw6jvdY8X6vye9pslqsvz67vD6nWmq+ylzsS0pi0z88bhfou7XD56JrIsRO6asux0zI8M2ekJsQzLyBAuI7aep41qBMeOH+QZjW/T6cCVFn4UrrBah8zHk590uzMDn95Tj9M7lOCi1tMjPc68EcDzCelWt8YzlgqSZrImLzrzFZ3uSH4OT5EgNb/g/fuiq4guw2+NLSqCMvOGxj5bWWOZuWrYoebuWbUVNz5tSoBn4y/XjEcFwzrbJtWgrPNw/WCsYRYNWni6XmaWbMFCXYhwCpqcbge/OPsdfytKcLHAj5+FGCVjrnRsY733uk1aV4sPEfqKOjXpBe0RTVpUICJ967G3sPhMYrnjx6pi8yG4I+HT5rW/DTgMwr1rBCuNXc0f595/UPtG1ZWPk7MjrnXk08akW6Y7bEUK5aBQ2y+IokK8CHKhSd0EU579fjuKLDZLJMHz9yRnYiqK5DsCqJTrQ9ratLDRnixshm3vM5ifdtqgMyzCxwifK/RdCX5GbbPwugfyU+3/n8m4uVrxxgCwbD368jcUVInIxLqOhXgb1eOwCn9yiLn/2/WEJzPTAgLs6w38fXJkmalVjbxSfNJ2hDXahK/xqfLvBzhiYYOzT5mAl8cvc+Z4bzuY60RCjjpp9VX4ncz65HTon3mBQ2xGnb0k1I1KdtW7N9DW3xRp9VX4AAjpF03uSfuntZf0wZmixdmmjTe/fktfPJCIfFJ/00N5toLK6y6vr4fmo0DYuaOjqrVco06yXX/XVP3d2wnsJk2T5t1YvcO+MmkHtz0+r4uSVJMk0n2Pk/SuRLo4fqkmZg7stt32Pmomn0H2MUO7hYhPravm+ev/Q4az6tjoWrS7KWQFrAwdwSAswZ2QueibEzqU6JJYxexViWkKJpvrVpzfhTf6GbWh6180nTX/nrGQJOUWiQp6s8V/q3tLw+cPygSWl9fjtVCBe9e9hwIm1BaadIyYtSkedkPEgkJaW0Ynyyjl27jWS/6sdWE1e5FSS0RzRkLJ/I/xHaM7NZB81uW9IEbwgNwz9I83D2tP/5y2QmOy2AH/g65GVh93TisWzyBm5b3AXXq0K3HaqJoFzhEdILFFtE+x37zUF64ch6F2UHUdDT6t7HJnZg4RUNS86/JDvpxQtf2TPn2gr9PN5nnzf19sqxpo+hGqdaaqNljwlq+W07vY1sPHk590uxMhssKtJvR2tU/UrasXXUW9cEy6/q8vaAA4H9n1ePBCwbhxobeGt+3OeO64ezBFTqfNLOFCc4xmS98WwUsUKAICWnv3zwRfSv4frR26PNnf+mL5k1yATF/Ezch+L2wDFGfkUhWkmQMHPL7i+pRnJ/JTa+3iJClGELwO/x4y5JxUUO/3YSKT46+u7ZCmqkmjRHSuJohe7NoSYLGv4yXLk/VpB1psqyPG3jPhpUr7pk+AKuuG8vZWkGsEvqhRr2Oa74uRcdKKz9B/bWn9y83SWm8Tv/dZ29/QOdC03KsIpTy7mXXvqMA7PZJi1WTlp7iTnrWmvAEnwz878X1uHh4F+aY8QVaNKWXo3zdTtYB+6AWrZELhnXGZGblTT/ROs583M4eXIHhNe3hlGZdw3Zun432uXxBRpbCpnBW1zvFaoCMBg7h9w1xc0f2I2H/UYw1cAjbz52YOEU0aRYmoOzkTUQ7y9Zd5qzmh9NohST1ErvJ0U9P7ol3bhqPswdX2NZDRdM2HE2P1YTbbNX51zMG4poJPTCkqp3muKhPmvqM1FVnramZ8/4tmZSbnxnAlL5lyAz4uNsXsJeZ9bmImZ4uOI2dJk0vBIcUsX5t5wdoheiWK2abrANimgbNAoPg62a3KFBqIjyxqGOXyERbgnEsCPpk0+1Y9BNPSRLf106PU9mOt++emSmjT5YjbW4Wpl/FrJ3YvO0i3Jr12XbZQc23hNdUORFzR+81aTzhQ2/9E4vWVi+kqFmZR3d09t44QZbACSxnPtawv622rWDzUMfgXfuPQtFpEfXEKmRR4BAi7fBJEiraZeOWM+o0x1QGdi7E5p+fjCvG1PAuN8X6o219rZ2/VGvE75NxzuDKyG99+8XSIt1aIhyO71VikzKKLEn4xdQ63HfegMixrgJ+SlaP1mqiaBeeWPQjKzL5tcrX8T5pTIlONGmRsOAWgmSQ2ZfIztwR0GmTTMziwhOt6JnIKi3zFeA1gSRJKM6zn8xqrmH+drqPmdmE/fT+5Zg/obthEmS3b5KK2u6qk73bID/qpExEg8eLIqn1SbM2d9RopWQz3xQ2BL/2XEhRhCYnbrRU0XqJjfdWZnwiz8LN5Nfqkg65QSE/PLV9RcYHSTJO5GVZMlisqPB8i0X9ngxlO00vcXzSrDRpqrmjbXRHPlpNmjEV20/NJuQddQuLVj5p6uJmvAOHiPLQhYNt04R0TavWne9jLAm/N5eMqgYgrkULlykZAloZAxmx5UT/ttSkMafKC8PflV37juLgsSaNeaWeWDVpbvcfTDYUgr8N4+MMOHr/DjfO5E72SdPTFjVpAN+/Zd5J3fDS5j04a5C4BkPPE5cNw8pNu3HGgE72iVuQpLDJ3RkDOqFPeQF+OHxcKHiK1RzGaqJoF51TWEhjkomY1enHbKdjuNPyVOSIkGalSYu+d0LmjrrgHKaaNJ3GDdBO8mNZBTZDdB8zFXbiMbZnRwypKrLUHpuZHepR+6C63YGZMFhZlIWbGmrxP89+FPGV4CHrxkoew7u2xzPrv0Z5QVTI1fQbGz9M/co1b1LKtq/++SmKuHDhFushnV1dN+/vYpNNB5Vqgde+p/QtQ0PfMgypbofzf/+2bR5OzB1lSeJGA84zGeOCnO0T3K7465+zyIKFIbqjiSmjn4kMa7tPmsljntC7BM+u/xod8zL4PmnMMbMJeYc87YJV5/bG75Lez89TTRrP3FHw2pPrSjGocyHe37ZXuDz1fecGeZPEhDRZAm6Y0gsTa0swoLJQvGyOJs1q7BIx4wa038rywix8/s0h7Np31HKsDecZm5AVa3TIZEFCWhvkpF7FeOU/e/CjEdWGc7yVdqdYr6ySkMaDt0J17aSeuHaS9SbddhTnZeKCYeLBT/R16Waz1xiL1Wq81eqw3YfGlZDmQpOWqBD8alNYmYSwH7lCgS0gNKZCnBVyIHy/bDr1ftlvVzwsQrTmlCLpo4kUJezLZYWoGZz+GZlNBBUlPKH6w9ovbIQ0e+HwltPr0LM0H6czIepFojuqTSbr+jSvGHbyor/HkKIIaWZimcfqFyi0QnP0uFV0NjGzLabvCuqNzAIuqIF6RASiSIhxEY2k5Gws0G70br94Y1c2i9WnVK2ivqZmpoysqarbwCGn9itD+9wgepXmY+Ff1xvOs8/CbEJdpPM1HtS5He44qy9uWL4xWlcX43rQJ9vu/xauo3XgENvrbZ6tE3NHSRI1dwxv68D6OYsgSUafNLZ+BnNHwcAh7PPoVBj2L969/yh27z9qWZ9gjNEZ3Wqok016ipZETDx44SD8Y+4ozBpRZTgnm3xgnWB1XbruVRFvNBrMOGgznOB65dFSk+Z+qEmYT5rD+9ZMGh1cGjV3tNCkMZM3pz5pkmRuTmS2f5pKPCJgaXymhCa60TQiAWtEA4fo+wSrSeNNtMzyiqQVEA4LsgO4cmxNZDKiT2vqk6YmEkhr55MmIojE8tz14xX7S3R1PdNm24VwXo6rxr0vzURTIFO1fd284yz/b/ZwnNi9Axr6lkaOaYK+tFziVpNm990Y2S06SY9oanVDkHl0x2h4f3ufNPP6jagJbyxt55NmtoCSxRFKztPttSoatZelXY5YVGbed8WJi4aduaRRSFOfE79cMU2a+3dbr0lj0VtisVW0+raxz6e8ZVzcue8ovrHRpIkskgzSBTNhoRD8RNqQ4fehb0WBiclB9G+3fgqijuREFC+EY69wW77VZWYTDxHzCzc+aSLXxOyTxiR3uoksYOeTFn0RRXzSDNEdTSZBXN8Gl8KmKEGfUVsgSqPA6rZof9W3iZUmjZc+cp6Tn5P7Etn0VR2bDSH4OckDGnNV7bmQoljuURi5zjaFOVaLKBo/FYt62G27EM7LeT+1E8IcadIcjCk87feQqiI8fskwdCuO+qdpNGktT8G1kGZz/vyhUYsKswA+qjZJXwWfHNXCm+2lFqmHUDsZj7H9w+zdFPFLcjOudzAJoqUnVr8mu8VK/WJRVJPGTx8vM2EVvU8aWz/9ghA7nxQ1d1S3UzjS2IwvvztsWReR5ziipgOeuGwY91wsC8XJJD1rTcQNNx9CPZbmjrY+aW3T3tEqalKicavJs7qONxkd17Mj/jZ7uGf10WjSRLQH+hVXpz5pbOAQF6vsVnVkBRsRnzS27rKZuaNsDA8OaFdEvVpE0UzOeR9swWLEtn4QG7P0H2l202zesGM3wef5kYqgWXF2EjjE5ObY9tXfh6IoQuNJLM/dqp3YfK0m2HYbmAMufdJ4F7ETTUdCmn15anFW2m82m6B2VVS4TjzsfNLYonibpQNRU0a9MMJq4c2Ci5iVK1JXtk6Aubkj66trhkGTZjGul+ZnYkpdaWSjezt4JupOpiys8MJboNQPd2pxvH4sQUKmC6HVCXpNGqvp07erSEAkQNvn8jMDke/gJ7sPWNZF5LUI+GRT3zUydyRaBV2YABGuhTSL6+zek3iJaKkeflXrV5PcusZHk2Ycagqzg0Irk8Lmjppr7G/CyqbeaYGONGkRIc38xtgoV44Dh5ho0vyy+SbXKvHoebGsYFqZ26iI75MmqklTuOn1sEK6k1eWfQZ25o56k1p+4BDGbFM3goZC/ABRxjrZJrG41vxirSbNPJ2QRsBF7+R1PbaNRPqmWm+hACww16SpDKsuivzNCxzifpFM+1v/5vCsNfRlqZpr/bNigxGZhek3qwcP7vjE9NOYNGm6ulu156yRVXjwwsGoLc+3zRfgv68CQ1QEtr/dfGotZuhMNfVPTe1PvHtQoMDvk23N+GKZ+lgtkunfHa25o9g47JOj3zc7IU1UQ2s2zqTrPmkUOIQAADwzZyS+P3QMVe2jodbjYe6YrOiOT15+AhYt3+h6Q954o3G2T/JY4nblzeoDytXgCH494hY4xGIlUATRgBXG68KJrT6u7IatWQITWNakzSfx6yNLEneRRO/P5jWx+AI06WNSc9A8B4t0+omDXbQvs60houaQbB3c3WNxHt/MKuKSJlAGO/nQz6mEQ/Cb5H32oPC+jC9v3o1/fbSLm8awym9SZ6tJktP9nkRb227zYZGx1tE+aS1JCrPNTZRHdOuAP/54KLp2yMEPh49HrzW5K1kSEwTs+qB+IUfNm0VdHNL3GT+jhbeN7igymea0u4hPmoiQZjDDE+gsPD82Xpvz+7ADnzSm/kGfbKibvkz1Vqy+Z5l+HxqbjfsxqnipSWN/6ask+q7rF9UKsgL49uBxfLbnoGVdRKPUmmrSUnyh3gwS0ggAUdU7a27o9t22Mq9Jlk9afVURVi4ck5SyRWDHj2SbO7p9RpP7lGJodRE2fb0fB49pPxq8ibp+U+JY6yNqbqFi/Jg7u2/R0O/GcsP/W2kRSwsy8ciPhqAgKyA0OWSb1ywEv9/HN3eM98crlhXMJot9c1TYlXMnC0R2flDtLCbaQGyRcB/78VAcPNaEinZZ3PNRczR7AZq9L/0iV0gwBL8ZpQUZOGdwBdZ8+o1pGqvuwxZtFd3RqbmjaHvz7p1tI5FxwlkI/vD/+v289Izp0REAcOh4dJw0uyW/LBZ50E5y1W+MHr5Ee5GqOdGPTT6fFBGsbH3S7GvKfX4i0R1FNj3XXyrS//Vjg1mbx2oyx5pLBvzGcVofOCS6FYcxL/XZ5Wb6ceCYuZAWy3RiUm0JNmzfGwl8xFbPar9Kq2+KfrwOL2gcsl2IEDM3lkyfEe2TRrQK3E48WawmP7aatDa4mTUgPtFMBG6LD/pl/PWK4fjz21/ipqc/0pxjn/vL147Be1u/xzRmA28rRDVubCqRSFBuooCZlufK3NH6mnE9i4XzNJo7csqVJG67yBaTfLcMrW6Pv773FYAYhTQhnzT2h3k6fXsHTcwE1b/szEzdaHZURrdM0s3zVoU0tgz7Uozjp5gmzQwhfza9eRl7jl1dtzR3jM8EiiukMW3kzCdNSPwAAFw7qQfe+OJbTK+3HuO00R35+Yt+E/VX639rAwRZC576PuNjzR3tfNKEFpWMadhnYSaMiex15SRwiGSSRpYBcGTR2EPwa7VN+vY3y8ssuiMAVHfIwc595uHrY5lPXD66K6ra52BY16KW+llEe5S192aajl2QliW0042zflnijvuxatLSNbojCWmEKU7f7V9MrcNDqz7Hz8+oM01j96I5se9uTbg1nYsHokJRZVEWtn9/xHCcNylkV7FqOuaipqP4/ms5GYLDFFOsGxMvpz5pkstnpk5QvLSR1/ubcH3PLHzVVLx6/84a2AlBv4wBFYVY9cke1/noo4vx0O6fZY6oJk2dh/A2JQaikyMvgiyZEd0nTftc7Z4PT5Mm4pNmhnqPVjlYToJZTZqluaO9Js3s3s8YUI6mZgX/3LjTcM7O3NFs4uaTo3tEuQkcUpyfiTXXn2SbXhvdkU/fTgVCGyA7MXeMPApRIU2WIuOWfXRH63oC/LZkBSCzviISBVT/zHn1uWRUNf798a5I+P7Koix0KszCjr3h71nQJ3MDpMSymTWg/Q4GZNkgfDnZJ02lpmMu3vj8O9PzsQhpAZ8c2VPQDrYYK42j3s+2QBe92O/jC2kityFB0kS7ZUlXn7T0rDWRklx4Qhes/elJqO6QY5pGRMPRFhEJJpAoRAf1lxaO4Zos8kJ+x7KanycopLGaBqHAIQ4czHmIBqwwK9fL56ydgPHz9TMTLbNrvYquKssSTu9fjs7ts7lmJqJ3LhY4hP3bPGdD4BCbSHFWfkX6ckXMsJwQDRwSPSbSP/XPLyQY3dG0HiLvkWT+Hmk0aTFuZm3WNe87byDyTQRqXvXZbHj3V5gd0ARMyG3xTRTySbNNoUW7PQU/zQMXDMKFJ+gDTHDK1mtldOd545XZ+6IX7NnNrBMS3dGlJk2SeBYSxrJuPrUWa64fF1mIyfD78Lcrh+P8YZ1xev9ynDWogps/d580B2OmnbmjPiv1vFl0RwDoVmy94OnlApL1BunRgqzMiGXdt0pvsWAX8dYKSTIXENPVJ42ENMKUeEQZtI3u2GY1ae4m/PFAdCzL8Ps0AS5UuJq0GKKhmAVw0MM2m8jENObojgxObk/9SHlpfsF++MzeW7PQ/Ox961dy7VCjk80ZV2OaJpaPo9PNrK0eu8Hc0WQiWFkU9r+witAXLiuan4gmyAm8faxkyd70jatJi6Vfi2hFrPZJY/62NHcU8EmzuvdmToAZM42ynU9aN52WP7vl2brZe9EOTWROk9srK8jCL6b2jblsjUm0jYZUr43wy+KbWYtNpnnfCAEhzdU+aWJ1KCvIwu1n9sWvZww09RXlaWNca9K45o766I5hDhxtNM2zS/ts03OAt/M4q++D1tzRSpPG/m00dzQbr4Q02eALaWaRjdMBEtIIU+Kx8GA/gLdNKc3pink8cVI+z0+GN8iKmKmYkSsopGn2mhMQgIzRHR1Vy/Vm1qpw6OXmmuwEx0xAZTek5dUHcP723XpGHzw/bxSundjTNI3IxOrq8d0BAAsn9gAA9C4Lh8Ru6GtvaqMxO7RIp9fwstovRQH+fuVwTKkrxb3TBwAw90lT5ylsM4tE4HSCKnRLDscFg0eaYHRHu3pYprGol3aDW4vAISLjg0Xn5GlczYVTa5+07iVaIc0sEiIPp0M3q1200xpPqSu1LtumLLb5I+a0JjelbxefLEee89EmO580m4ro6sIrMzypNqYRiu4ooElzC9fc0cGgqV204Jk7atOr54vzzQPR9KsotGzzRCmQ2HKsgnToFwsKdBYLbBtLEjB3XDc8cekwobFIkowLDOH6pKeABpBPGmFBPLq1/WbWcSg0DdAIGGm0dML7OLCDbKdsBYO7leN8w34w4uRl2u8Tpq+LG02a4+iOmn2yHAhpauAQDz8c7L2ENS78cnmTUvaddOqT5vfJqOtUYJlmbI9i+GQJAzmbt6pcM6E7pg2uiKxg//nSYVjz6TeY3Md6YgroAiJYjC92mrTBXYowuEt0Dyv7wCHR/DwX0ji3IUv246N+pTukKDFq0gTeI4v82VPZQfM2EtOkmRNyEGiAbSJe3ftXFOIv72wXzo/FsZDGjAF2WuPfXjAII+94BV+bBInQj0H6qmi+MTaaNEN0RznaVsc9ENLs9kmTJAlBnxzZXFvF1tyRk7eXi5484cOtiTjX3NGwT1qYbsV5eGTWEJQXZmHystWaNEU5Qbz2k7E42hgynAO8FVKtblU0uqOk+1bpLRY0wVVkGT+ZHF4A/PzbQ7b1y80IcL+rZn5q6QAJaYQp8TC7s5s8t1EZzbVWJtlwhTTmQ9YhU8Hd0/oiEBATtHjkCvukRXHjk+Z0Mqs123ByXTixp4FD2NVJk9m8WeAQFq980lgKsgP4+JbJlhMsSZJQWRQ12ynKCeKMAZ2E8tdooS3SGQKHsJo0TnrWoZ0XtIPNLtNCAHFDNAR/9Fj4vbI2NTOYO4bCbSsSdIRbDxcmfmZXjOlpHtEyQyRwCHMD+mq5jQbH9olbTu+D9dv34pzBFbhh+UZDWqHABU7NHZnJY6PNnoDh8OJW75B1Wbx90szqqzdX88myeJRd1+aO2nsL+jlCmitNmu0lBsyGQZ4Zn5PXik3LM3c0dGPm/Lhe5tF+u7TPwb7DfJNILzVpokKadXRHbT/UP1O2H2jnReZlV7TLQk3HXJw1iP/NIE0a0SqJh6xgv5l12xTTtH416TOg8Mwd2VU0Lz4Q+S580txEd0zYPmkt3yAvHZnZD5+VuWPP0jzLfOL1+nnts8Uiun2Fvr3tgn2w0R15Ag5blogmyAlq3hl+H2aNqMLh403oVJhlOhFT0VdT1azx9n0yC3XN4lYrEr0+em5ibYlpOqHAIRbneL4yZt8aNiXbJy4eUYWLdWnZCaSQJs02hRan219YDRkG/ybDeeN7YnZLxn3DJNNop3pE2oB3HyJmimJCmr4s5+Osmf9jrCbqWn9IYyAn/fzHSd0lk6olaj7BNo2VUKQ3dzRsnO7jj+dWbTF7TA0uPKELAH5E4HTdIw0gIY2wxPuX29bc0fMS0wOtKUoSK+IUm4+tF3KIqE8aWxk3Jl6OfdJcXhvZJy1emjRJ4ps7ShJ6lOTh8UuGoiQ/k5uP08AhqYDWb8s8nSG6o82EL+iXEfSFhZvsoA+Hj4e1WLyFpKygt5MAdjxYcnof4ev0dVN/8vZ9Mgt1zSLyHumTsM9gYGUhrh7fHX3K85EdNH+PxULwM5o03cDD2/RcX68+5fn4+Ov9mtV2u/tjzVhF5ufxnhBbfT/t9tHTRIBtqafZxFc/cZZlCR3zrDfojqR1aSKrL5M32RaJoioaOMQNXOHDwZDJCn+SZAxmoR9anFTdrN2d3P8JXYvw1hffm563WkTX7okopvGVJWOfZvuGqCaN1XDy+pZV0KJUh4Q0wpS4aNLS912JK/qIR+kCr6Zea9JE/X3YstwIaU4nWE6DOqioz9dLTZqmzW2CAZzY3dzsLB2FNNf7pAlov+6Z3h9vffEdzh5UgTN/+4ZpuV77pLn1IzNGd4xq0gC9Js14TE+sPmmSJEWCwVghMvm26prlhcZofPp6/W32CHz+zUH0Kc+PHLN7B1k/unho0pwiuiedvi6SxJ/8muXG06SJCmluta/6YE+NHMHbbtsMSTL63Xrqk8YLHBJDfvp20I+/TuYCZl3ZSR52rgVWazoanzQrTZrG/96oTWQFPFFNmtZEkrMAQJo0ojUSj8UHe58Y78tMB9yaziUbs02TVbwYGkU/smy6ROyJoneAFqW+SztkBmQMqS6yTyyIXnvJe49EJv7p+P5pfNIs+oqVJs3svk/tV45T+5VrjrVr2XaCXSz2PgQ//7htCH7deXXSx43qKaIlc2E2bKfR4RFr+10zsQf2HWnEoC7tcPMzHwEw3l9W0GcIcsPb01F/jYrIOBTvsdvqcVjVT4I+OJW1Jk3vU+STJXTMFRTSBJ4/15dZr0nj+OgJmTsaAofYXiJMLHs+8rDzSXNSd3NNmnUmkuZ74f5uNNE5BTez5pk7mmvSLIQ0m9V/L7e7STQkpBGmuPnY2mEf3TENZ4keoNGkpdGiD1eTprEpT05dEr1pupPJ2fjeJfhoyWRvzR05EzA9IpNyN8El3ODlxEl8nzRdYAKH7b9s+gC8sHEnLjuxa7gspsd5LaS5Xf3XD5/je4f9wHiLFiITF/Uya+FXvH5miATRYb8N+uoUZAVw7/QB2Pbd4cgxkXfSrg1YDanIE4n3+poTTZqiOSfpFnIk7jUqhomz5MTcUSQNb9FA2wd4Y5GdkCbBOPa78kkTCBxy5sBOeOU/e/C7mYNjztcML7qT3e1LiPYVu7RWi0TstVbmjnrTfP3z0kT51FxnXi/9+NGrNA//2XXA9Hw6kb41J+JOPD4488d3R16GH1eM6co9LxpuvbWRrM2su3bIiel6/oooa64QU/au65IITRqL02fmtfmFZg8kSeJ+TMU0aem3SCKq0TSYOzLBKuw0VAAwdWAnPHxRPXJaTILY7Lw2dzTD7vGom4sPqCzEr2cMxOWjw+Msb9FCKAJqS9uK+qK4JUcgOqZIz2Sr4ibCqx6NT1oSzB2d7Plld7v6qHqAVeAQnSbNJ65JE2kEEZ80HomK7mgG+22bVl+B9f8zESd0be86v0bbwD3m7+3IblpLDLeaNE15Nuc9MXfU7ddnpUlzGxTqn1efiFP7RffYTOfojiSkEabEQ1aoLMrG+p9NwqIpvbnnfzdzMHqU5OJ/L6r3vvAUJllC2vNXj8LKa0ajrIAfSMIOXiRBrYO6s/xqy/LtE5nAajYSrUlLtoWqIXCIS3PH9PRJi/5tpf037JMWo6DMTqCyYgjBf/e0/jh7UAVKLDasFaWyKBubf34ynr5qBE7vXx5ZQeZNgEUi1YmMRcZ9qQQry9bFJ2PtT8cZjhcz2htt4BA+ouZRKrZCmsYnzTY7zwOHODHd0/d9Sfe3rFnI4V+jYgjB70CT5tbcUWR8svNdlCRef/TumbDvUYZfjjnvRk5wFBZe9q9fNxrX1DVhaJVeSOPnYfeas/cQy+2wfdVKc6Xfr0//rTbzSbNqa70Q5pMljT9prFE5k0n61pyIO/GKVGU1GNd1KsCKa8ZggkW45taIZnKRQAEjO+hH9xLrsOxWXDu5J4rzMjTBAWIxd/zblcPx3NyRuHVqHQCgEycggBnppEnzGpFgLSJ7XqWfiCZu7ijLkuY8uzdXvgsNPtvOIiHkzTh7cAXuPre/ZxOJrKDPMHZzNRci5o4RjYvVyrg3fb+iXbYmr41LJmH19VHBTWT9QNJMFO3rZWemqjF3FNGkeTwMOOkS1v5qJtEdTfLnBQ7pIKhJc2vuGD9NmncPhc3bjQmdXiPNi2DJwqt7+9wMVHE+2d5o0qzTWu6Txobgt3iWesHLUpOmWXSJ/t0+J4iLhndhyjM+C9nhWJCqkJBGmJK+3To21BWY9jlBm5TeIQtMsuOJ2yI7FWbh7RvH4+rx3SPHNAKDw/yyg370qyjE+UM74w8X1+O5uSOFr9WaOiV2aEt2hF99xCxumhTySfMS0cAhgLZvBn0y7p7WHyNq2mPBhO4WV/Fhy/LCJy2epqaigUOeuGwY2mVHBVY1yahuHUzz9rLvs1nlZQY07SrSOlrB2f6ZXDS8C3qV5mH+eO3zV02lZo+t4eZthtfDgCNNmk3f55o7mtSYN3EuzA5o+owaCfCqsTUayxexACuc+okIaQKCkb7NvOyfshytp4jAaAcvgiWLk6qbNbvd89BoXG0KFDV7tnqW+nR6IVK7eMTOi6J///myYRqrG96CkzaQWPqKOhQ4hDAl2dqBZPG32SNwz8pPcN3kngkrU7tilF7tbly1j90nzSdLkaAHwvVg90lLcBMm+5lphHzZvbljuvuk2d2hT5YiE6OsoA9nD67A2YMrXJXrtU9aPFuev3eQceIyoqYDnp0zCqN/+armujMHdkJOhh/tc4OY9tCbmmsSpfm3ChwSOc70gAyBZ5KXGcCLC0Ybjv/6vIH4+Rl1KGIW6sQ0ad62hZO2tQwcAq1ZmfqXqYmcJGmi/vnk8J5e+Zl+/NCyofqLC05EXkYABdkBfPVDNGCLSBPwtPpCmjQBIU3frd1tZs1HQjhkfDMUV+bSPUu15vz6DeYN5Tmoulnfs9OwspfZBw4xR23ngM+4/xuLT2d2qxewzKxC2L99kqTTlPE0aUyepElzx9KlSzFkyBDk5eWhuLgYU6dOxX//+1/La9auXYuRI0eiffv2yMrKQq9evXDvvfca0v39739HbW0tMjIyUFtbi6efftpx2YqiYMmSJSgvL0dWVhbGjh2Ljz/+OPYbTxPSTFbwjNryfPzvxfXoWereDNApyfJJiwde75MmikaTluBoTvHexNYOzSq5xcTLjkTJaF5GjpUF7l2FbafsGPzI9OXG4pOmItL2bp8PbwJsJrRrJ23hH7Is4eS6Uq75cWFWYiwOxMwdo39nxqDpkGVJI6ABwOHjTfbXuejWVqZYhj2/LN4by3dK4gtGZkOCrNNwqPVgA3tlBXwoaNG6OnkHw+Vy+qOQAGanFeLtk2ZfH1EkCcjPCkT+d8r0IZX46cm98MycsIWInbmjF98V3rivmq7Wd2mn6Tf25o5WmrTw/3ZaK73/tL5fskG1zHzSJElrus4b3+yEuHQhqTVftWoV5syZg7feegsrV65EU1MTJk2ahEOHDplek5OTg7lz52L16tXYvHkzFi9ejMWLF+Phhx+OpHnzzTcxffp0zJw5Exs2bMDMmTNx7rnn4u2333ZU9l133YV77rkH999/P959912UlpZi4sSJOHDgANoC6S0qpBdah/dklO9dofEKwa/aoJutuCbXJy2hxRnL15jx8QUGkdXEtA8cYtOPWXPOWE0UtQKBF5o0+7bvkOdOIOJN1ET8NPQTKJ5g1yHXOyHN6vGxXdPsOZv5HHrBD4eO26ZxFTSFM6n9n1NrAQD3Th8gnI/+0bA/q9vncAUcs3b0SdrpulpHdmEj4OdPpkVmDm590kQwmjt6uyB0//kDcf+MQcI+eiw+WcKVY2swoLIQgIC5owdV5zXr/5s9HJeMqsb95w8yRpixwFKT1lKQ3XdGI9DLRqHaXJOmXTTQRJPk+qTx80w3kmru+OKLL2p+P/LIIyguLsa6deswerTRBAEABg4ciIEDB0Z+V1VVYfny5VizZg0uv/xyAMCyZcswceJELFq0CACwaNEirFq1CsuWLcNf/vIXobIVRcGyZctw00034ayzzgIA/PGPf0RJSQmeeOIJXHHFFd40QgqTbO1AW6I1adK0jr/eTfpvOb0PFk7sgZ/+/UP8++PdhvP6qFGJJNnPjP0IFeUGuR9TkTZJTyGNXQm2hjUvilWT5lV0RyeUFWThdzMH44rH1zm6jjdxEtGkGSb+nEs66KL+xfLdkDS7NmkREWI1e9d54DPEopr52dXAKbwJ5I9HVeOCEzojQyf8W/ukGY89N3ckfvvq57hhSi/u8zbLLqpJU1p+h4+z7wxr7seLHGmFiE9aZkDG0UZrTZMByaht89QnTZJiCrmvx87c0YvvCu99rO6Qg5tbFgKciNeWgUMi5o7W751P953WP3ftZtZmApv95tmaIEIejwWJJKV80vbt2wcAKCoqskkZ5YMPPsAbb7yBX/ziF5Fjb775Jq655hpNusmTJ2PZsmXCZW/ZsgW7du3CpEmTImkyMjIwZswYvPHGG1wh7dixYzh27Fjk9/79+wEAjY2NaGwUGeDjh1q+o3oooaTXO95IksM2sSCWfJqbWFMaJeHtzpoxxFq20twc+VvyID+WnICEUIhf1ya2DV303djqmfhnpi0++rEvzPRBCRk//qFQs20dQ6FE3Yd3/S0UivY3paUdzPJsZlVpoWY0Mtc65eixqGbFj9jHSnYCZJXXST20k0SRcnkTVfYYmwc7Fikh7X2Fmo3tVZgpa9Ioivs+pFj0i2amLs1MPbR1j/4d9Eme9uWGPsW47+VP0S47YCqwhZqbHJfJTjbZa2UAjTohhR2nO+QGcWK39nh6/c5w2fpnpYTQuyQHvzmvHwDgCGOuqT4j3vMEAAkhrdDXMnZoopiGmiP1CzF9prnZfpxhx6tImbox+8lLh+LBVV+gfW4QT7zzFQD7vi5B+/0BwmOC02fSbNIuzS6erxXHG63HH96Y7Xgu5+B9tJsHWJ5veaZ+2fq9U5hn39zcZPhWseKUxHxX2XG+ublJ8xu8bxtTjs/kfpKFk7qkjJCmKAoWLlyIUaNGoa6uzjZ9RUUFvvnmGzQ1NWHJkiW49NJLI+d27dqFkhJt0IGSkhLs2rVLuGw1LS+fL7/8kpvP0qVLccsttxiOr1ixAtnZ2bb3lAhWrlwpkCrcLXbs2IEXXtge3woljZauryh44YUXYs8HiCmfQ43RvHbt3IkXXtgRQ52cc/SoD+o6WmztARxuAth2Eetz4uzeLUMdytm67jocLXfjhxuQsXO9QG6xPr/w9bt3Jf6Zsfxnp4Twpwj4dOM67DsQfZ4qr7z0EnJM3SjC9/Hdd9/H/PxFaG72rr9t/CZ6719++SUGdLHqc968r4C2v7328krEuljr5B2cWyvhyc9lTOsaErqP/XuN/WHfD9+D9x79cAxQ7+v999ehcWt0YsaOUyrr1r6Gjb7o8QP797tu21CI1wbhfLdv/wovvLANALDlS/4YcJCp3ze7vsYLL3zlqh5m3FYPHGxswtIN/KnTm2+8ga8dujI3NYk/973Mc1zU5zD+/dVRqO2wdcsWvPDC51Dvf8+ePZr8mkKA/j3/9mj0GMv2bdsQCkmRstauWYPPsoH930fb/d8v/iuSnm33N15/HTts2uCTHdF3VmXjhxsQ/Hq95lhDAfDOnmha8/YJlx1qbsbqVa9p7mnTxx/jhe8+sq6Qjv9w6gcA6z/4AMo276wNduyMtieP//7nP3hh/2buOf4YZ3yWe3bvsuxX7Du38+uvwXuvVBobzfvq1pZ3svHYUcvyPt8ebdtXX3kZzUy/BIBdO3dE6nDkyJFIXpt/iF636rXXsPVA9Pfra1bjE5277Nat0bbdtTO15rKHDx+2T9RCyghpc+fOxYcffoi1a9cKpV+zZg0OHjyIt956CzfccAO6deuGGTNmRM7rVbyKopiaYViV7SSfRYsWYeHChZHf+/fvR2VlJSZNmoT8fPeb9HpBY2MjVq5ciYkTJyIQsHZ4nf/mCgBAZWUFGhrsBeZ0RL1HWZbR0DA55nwAoKGhwXU++4804sb3whHVSsvK0NDQ33Vebrhr82rg2FEAsd0HABw61oRF774S+S3S55zwwr71+PD7PQC0df38m0NYuuF1AMDgQQPR0LfUNq9Yn596fXl5ORoa+jm+3iu+e2sbnt76HwDAKePH4N9PbcDOxXjHagAALiVJREFUwwc1aU6ePFHj+M+i3kdhu3ZoaBga38oCuOG9l4CWFdRY+1vj+q/x58/Ck7CuVVWA8oVpn/PqfQWAbw4cw9INqwAAp50yJWbz8Fs3vgY0Hheu23wHeT/+9TvYenCv5lhJcUd8uv87Q3k79x3FkvdXAwCGDqnHuJ4dI+cOHI2OUypTTwtfq7Ztfn4+GhqGO6hdlOvefQnNTdp+oebbqVMnNDT0BQBs/PcneOXrrYa6/3D4OG567zUAQE11ZzQ01LqqhxVbvo2OM3pGjhyJfhUFjvK7/eNVONgYtsCxe+5/2PYWth8KW+g0TDkZW1d9gRU7vgAA1NR0RcOkHpH2Ki4uRkPDoMi1Tc0hXPv2SwCAovZFaGgYgu0/HMatHxjnPV2rq7Du+x1oPB7WVowfNxZd2mdj5cEP8dEPuwx13Xu4ETe19IsRI0eiv00b7Fi7Bf/Y9qnmWP2ggZhSZxyzJzaHcPS5TTihuggNA8q5+UW+5z4fxp80Gj//YE3kXN++dWgYUmlZHz0jDjfizd+8jm8Pav0Q6wcPxsTaYkd5WfHXPeuAveF3kI2mqVLbuzcaRlZpjlnN5djxTaXcZj7x0/deQmPLWNypUzne/874fFVuev8VoLmJe37Tik/x8tdbkJebjYaGE03L+/zVz/HiV58DACZOmIDmkIKfvb8qcr6qcyXe+Sa84Jmbk4OGhlEAgLzPvsVD/3kfADD+pJOwfvte/PHTDwEAE8aPMwQ1+vDF/+K1nWGFSnWXSjQ09DGtU6JRrexESAkhbd68eXjuueewevVqVFSIhUOurq4GAPTt2xe7d+/GkiVLIkJaaWmpQWu2Z88eg1bMquzS0vBgsWvXLpSVldnmA4TNITMyjM6kgUDA00lqLDipi0+WU6be8cSre4wlnyCjuZdlKQntzthvx1h2pm5l0Ov+z0YBY/MNBKLDWUbA77jMWOro9yX3XVGkaJuUFuZwBYbMjKCmjbj5wLv3wRrv+ht7Tz6fD2gS63OxllteFMAfLq5HboYfwaC3EQ69fgYBTjCZIOPvxJYXDDQzx7XvUUaIE4BEV1dJcj9+sbkb8pCi7xi7zYe27tFZbmacvrtWeerbSyg/k/GMh8Tcd0YwiKA/2vf9Pp/melnSjkl+P7uFQfgZBfz88vw+n+ZZZGaE2zI3kx1vmXZnur9PVw8eAZ9xHAqaPK9AALj73IGG42Zk6N7FgN/5M+lYEMA7N05A1xu1GiE3z9eKxafW4owHXseVY2rw8OovcERn/mjVlqLfVZ/Nt4n146yvao9/fLgrkr8V+vOBlvEkYFOeT46OO5nBIJp05o4BZlxi50JBpq9mBAMIMH0/Mxg0lOln8gm66APxxEldkupNpygK5s6di+XLl+OVV16JCF5u8mF9wYYPH25QBa9YsQIjRowQLru6uhqlpaWafI4fP45Vq1Zp8mnNeBkmO1VJlTvUBj9IlVq5g510xCMMhVn76KNGJZJkBw7ZdyRq456f5ec6eAuF4PeyUglCG5o5sWWP712CYR4FEohnzBbRzawB3VYWcYyUFwun9Q9rVGo65miOs33Bi82GeVgNLW7ax23kOUkCfD7xvq9ZuGnpa2bjpD56njqmmwXIYbMJCfRjXrleROCTJN4+ae7y4kbDdJeVKb3L8vHxLZNxzcQeWn8/tbw4heDXlhH9+4JhnXHr1DqsvIYfuE8kBL+TcPeSbIzMyF5vFsQovI+fdeAQ2eZ8upBUTdqcOXPwxBNP4Nlnn0VeXl5E+1VQUICsrLDqctGiRdixYwcee+wxAMADDzyAzp07o1evXgDC+6b96le/wrx58yL5zp8/H6NHj8add96JM844A88++yxeeukljTmjXdmSJGHBggW4/fbb0b17d3Tv3h233347srOzcf755yekfZJNinyP40qq3GOqTH68IFnRbtliEx1yN9mPb+/hqFmOaVhtgTYRmWB5gZftpQ3B712+iSbRm1mbbmXB/K0fl/QT4Ip2xn3T4vUM2AliXacCvHHDSWivC/+vCcEfJyHN60U0v8s9nCRJ0oUrd14vsyv0eanlmG3aLjK2aPPn5OHRRFq/sOBllGqbLcBcoQol4S1BtAElvPiMOcnD75Mx84Qupue9CMHP4pMkhGRtrj6TPq2P9MiOBwGbEPzpvE9aUoW0Bx98EAAwduxYzfFHHnkEs2bNAgDs3LkT27Zti5wLhUJYtGgRtmzZAr/fj5qaGtxxxx2aaIsjRozAk08+icWLF+Pmm29GTU0NnnrqKQwbNsxR2ddffz2OHDmCq666Cj/88AOGDRuGFStWIC8vcZscJ5O2EII/VbRWyW5qbzf8TM7NaDQAbUyTpo82xwtXLrQtQYJC8HtZTGvSQscLnkBmKhywK9a6JGwfmj2mBpee6M76xQyrLqpfQCjnbKzNXp7B0Ux4gdMw+HY4WVDSp2TNPvXnepeZ+8Gr44PZuOWTgWbmJVX7itm2Fdp87F/uuO6TZgjB792YEM9vG29RwYvSbDVpDvISCcFvt5m1/hrDQhC76MaWzfQrnyzp+qeNJo32SXOHlepU5dFHH9X8njdvnkZrZsY555yDc845J6ayJUnCkiVLsGTJEtu0rZFkCw5tCc1ARe1ujUn7sBP0xAtpCS3OwJCqdvjHhq8j980Obyf1Kka77KCQCWiiNGleol1tFbtGZCPnRCPyTXKLM02auXaGzWdSnxLuhr5x06QJpGHrG0zC6rmbRYJYTLFyGKFJFSCenzcKKzftxpVja2yvN3tWPklCEzMYqO/LGQM64VcrPkH/ls2Y7fIxQ2SfNDdIkOK+T1q8yORoKb0w2/dSsLTaqzBq7uhg0UEy9h2txsxkjJIkzXYqdvukudVWpwIpETiESE1SbxrTekn2JD9exGPeadZUbVmTNmNoZ+QE/Rhabdxj8v9mDbG9fvEpvXHvyk9w+5l941E9A/EydxR97Klo/pIy5o6a9jQ3HTN7t2PRZlpdK7LReiLMHa0mzm7M4ZxoHvTvDRutVT1X16kAdZ2soyuqTWn2Hsqydj9KtY6VRdl4/+aJyMvUTh1lgX6hz19PXkbsgR0kKb5+lPEc5TM4Qpo3mjTr806EOEtNmmruaNOf9d9p4xjD/5sdIMPmjtHfvDLZ++b5+6ULJKQRprQJTVqK3GOyJ/nJXP12itlHxSrgQbxJdKASPQGfjLMHR6PTOm33S0/sih+NrE64cOsFbswd4xVUIhYSHTjETIPDHrXuDolRu/Yuy8fmnftxziD7yM/s88/w803zYsWqSdwIqI40D7rfuYyw5M4nzcTc0USTBgBFOcZIphohTaBcNv2p/cowuEs71HXyZpsifV/3dkEojpo03piUiMAhDvK665x+mP/kelw7sYdpOU40w2FzR3197DVpPlnSLNrwxjf22tyM9BV10rfmRNxJtuCQCFLlDsnaMXbsoj3FkzSUbQyko4AGaLUX4uaOqSikxU/o4WnNajrmctNqo2WaN6gvDlEUeMU9fdUIfPXDYXQrtvcFZ6+PlyDutU9aLO8dq9Fyk4tZ0fpFJzttC5tcSJPGpB/Wtb1lsAqnGH2cvBvX4jlEcs0dPSjP9jV1UMYZAzphXK9i5HP221QXRp2MrbJk7FummjTNdVpzRx7spdnB9BV10rfmRNxJzymbM1JFDm0LQVq8wtTckfk7HhNIKyhgRfIQFSpYkuGvZEc89VI8Te9Fw6vwzYFjGMNsVg3Ya9IuO7Ea278/YrthsVdkBnxCAhqQIHNHiz7mZlIdy4JBPiukOShb7Wtm74tTwUZrBivm76+S4eG7KKG1BQ6JvTy7OjstgSeghcsJ/+8oEA6nbtrxh6+hlSR782d2zMsxCXiTDpCQRpjSFgQHmlyHiV87eJ/v4C7t8NyGr40lJdPcMcW6UTy1MqmG1txRjHhN4GMijo+MN3EK+mUsauhteR3vG3DTKbU21zirm+Za95e2XM9M/uMV3TGGszxi0aTlZrA+ac7zMSs6FrlJpBuz43PAH/vgedmJ1fj9mi248ZTehnvycmyO52clbpq0BH2b1H4cq5WC1ifWpCxJQnOIf46XTw6ZOxKtkdYso3Uuysa27w9jrG4VORVoC8JxLFwwrDN8soQTdJsIJzW6Y6pJaW0IN4FDUtEnLZ440SyLTJJSFba+QV+cVs89Nnd0FjhEWwBr7nissVk4H3URx2xxLt6uDuwte2F6fGNDb1wyqitKCzLD+UvRSLWxfE9vPrUWtz6/KfI7vtEdeZtZx56v/WbW3tzTyG4d0K04FyfXlcaUj9neaPo0zXaaNObanAzSpBGtkNasZfrrFcPxjw1f49whlcmuSqsmHsoBv0/GhRwfBnaClniftNR6V1Jdj+Zla4l81PUM0IUQTwXiG91RPC077rv5BsTybGOdMLLXx80nzeIO3dQ+lsAh7L5lB4+JC2mR/EyaKJZFLjGfNEaT5oGQJklSREADWgJLNKt7wbnP95JR1SjKCeCapzbEnJcdPE2aF3MweyEt5iIAAD1K8vDSwjEx58NWh60b268kyd5ahH1W6eyT1raWEwlHpNi801NKCzJx2eiuKMiKPexvayBez7oiJ4HigkYDkNjOm3KvSqpLaR4i4miu8s+rR+GKMV1x4ynWZn7JIL6BQxxJadE/U65jW8NWN34+aVbnnDdYr1L3UQ1ZofTQsSbh6yI+aSbnYxLSBAafeAvT7HOI9VvACpHxtHLhCmkeFJdq77BtdQSenSRJtsI9RXckWj3pZurSWmgNzf6v+Sdiw7bvkblzQ8LKZFcdnTgvewGZOyYPJ5tZ9ykvQJ/yxAS8cEqi90kzw4nQ6zUx+6QlwA/FaqLupr2uGNMVx5ubMb53SQy1Ag46ENJUzCbBbgSbcT074otvD2Fwl3a2aVmftHgE8WH7e6x9WKNZjuP7wA0ckmIh+BMBWx9N1FDdCDl1QCf89b3tOLE732WFXfPKpsAhRGuEfKPaDl4/6d5l+ejWIQsvvJBAIY25iba2mXVbxs0+aalIovdJM0M7SUqv9pQkCVeNrcHeI42o6ZgTnzIszrl5hpkBH66b3EusbIvCnQhptptZu3ju/zdrCBRFbMGKTRKP7TB8HmrStD6vidWkJSJwSKrN80TH86ygD09fNdL0/FHGR5MChxCtkpSMgEYQJmgmlwkX0hJanC1tyNrRVeCQVETETMwtzjRpMWoOkjzpu/5kMYHHLSk2p43gxNxRhZ0Q++XoBtZu5CZJkoTbhu1jTnzyRJG91KRpBL7Y8rKCr0mLPd/0W2jh/+2Uw4yQls5z2fStORE3rpvcE71K83DpqK7JrkqbJM3G1JRBTtDHlAc9s+ThZp+0toajvYuYvxM+wUuDx5fMPmalWXBj7sjCCvJxj+7IZB8PnzQv7yWZmrRU3Cct3piNP+WFWY7yOXI8KqSl83eBNGmEgTnjumHOuG7JrgZBuCbRk8tUW61M9X3SvPxoyh6tvCabeD4yJ/1Ts5Ltoqw0fgRCWDVl3N86Ttn1XdrhvS9/wNmDKoSzUeupj7J4rCm8+VS8zcXZ/OPhk+Zl4JBETfDjFTgk3awLNFuAMF2jpmMu7j9/IDrmZgjlc9TBlhSpDAlpBEG0CkLMLDfRk/WUE9KSXYEE4mYz61Qkns/MmSYteZrJdHh+1j5piX/zHv3xUHy4fS+G6faNFMHMjzfeQprXIfj1sFnGeiuJ0qQlLXBIir10GssI3dt2ar9y4XwOH28dQhqZOxJEipFiY2bawG5u6WvjmrS2hJt90lKSeAYOcbIXV4yayXR+BCKw/a2+SyGu7xebmWGs5Gb4MaJbB2eClaLuIcb3DYv3eMZmH4iHuaOHY4JGKxfHGXOyAoek2ozDK5+0I61Ek0ZCGkEQ6T25bYFdxE5UdMcJLWGzp9WLmxolghS3dvQUjXlMGnfjuAYOcfl+J3y/wTQYh9gqnjekEsWMq0y8XzuvW4fNj91LT+vT5XGh0ParuJg7elh/7fgSv/7Jq2cifNLG9AiHsC/JFzMjjDfsPcfS3kdaiSaNzB0JgmgVdMzNQHWHHMgSkJ+ZmE3Kf3/RYBxtDCErjfdhSXdai7ljPHG7aEHtaUQ/cWZ/xXtxxCsZIbKZNZOf38cXbOKx4MWapsd7n7RYI/0manwpzDZ+sxIR3fGWM/qgtjwfU+pKYy/MA7xadLtoeBes/ezbiBCarpCQRhAewBtg3ZIOq8mpiCxLeGnhmMjfiUCSJBLQkozGBCmN3514TvDdbu7uZoIeyxNIh8enD6ySDnU2g/3W+E0iIsZDe9Qcinb2gN/7/LX7pMWWl9b8Ln4Pe1DndrjtzDoU52XissfeA5AYc8fcDD8uGVUde0EeIXvU3pP6lOK1n4xFp3bOokKmGmTuSBAx8JfLTkD/igL86ZJhya5KTFwwrDMAYFh1UZJrEhs+WUr4RtapSDxN51KNZG694CXxfGI+B9qKzIAPZw3qhIa+pahI8wlOPNDPG9Oxy/EWBHIzo2v27BjqVsC3oikUX02adp80D33S4viwJUnCBcO6YGDnQvZozPkmes9QOwpsFrS15o6xlVXVIScugWkSCWnSCCIGhte0x7NzRyW7GjHz45HVGFBZiD7lBcmuCkE4QrPympZT5jDxjAzo1CftnnMHuC4rlklxOjw9K3PH/Mz0nVJ1aZ+Dj3bsB6Dz6YrDJJ/VpMVjUc3noSYwUT5pXpbBbkyeapre6UMq8cZn32FMT74ZYqI0l+lC+o4oBNFKScawJMsS6qvSW4tGRGlbgUNIk2ZHPLQheoZWFeGdrd9jxtDOcS8rmeijz0kS8MCM/jjaBBTnZ8a37Dh+Hcb3KsY/P9wJQCvkxFuTFo+JuJeBQ7TRY2PLSwTtgoqzUeHRHw3Bvz/ejfY5Qdz/6mcAUi/ycIbfh4dmDhZKm87juVeQkEYQBNHKuHf6AFzwv2/jpobeya4KFy+/vb4ET6LiRTwF60SYAD92yVB88c0h9C7Lc51HOqyc82o4qbYEgUBighV5AWsOvXHJJBxvCuG7Q8cjx+K9Z1pzKOR5nizafdJi1KQxfydC4JGYujsdE8b2LMbYnsV4oEVAA9JP0NEGakmzyscBEtIIgiBaGUOqirDplsnwp6g9/i+n9cPsP72P60/uGXNe2okTfdR5JEJIywz4UFueH/dykk0yNRNeFZ3hjwY7ymuJhJuTEZ0O7j3cGPk7Hvfb1BxfVb+XCzda/7bY8hKBrbvbVtIGt0mvMVFjXpqan6+EQk1AEKlGeo2pac3ccd0ARAOntCZSVUADgJPryrDp55Nx1dhuMefVWvZJiyfpEkwnHWqZTGVfrGX/alp/VLXPxp1n9zOc422mDMTH3NHvYHN1N2gEqxh7lVfRBsXLY4Q0l1Iae88FWemj4QW0Y0A6aNbjDWnSCIJosyyc2AMNfcvQs9S9iRbhjuygN5+fRK90x4vRPTpi9SffoKZjjud5J8Inra0gacyx0otzBlfgnMEVpufvP38g3vj8O0zqUxI5Fo/AIVPqyvDnt7bhhK7tPc8b0D6XdjmxCSmJ9nlltUchl1IaW8/iFNmkWpR0fr/iAQlpBEG0WWRZahMmWq0ZWaNJS9/P+rLpA/Dku9tw1kDzSbRb9BPtCb1LTFISbZlT+5Xj1H7lmmPxEPAzAz787coRnuer8gNjrtkxNzYhJd57xlmV54W5Y0mcg9l4TWsZz70ide1hCKKNkm425ASRTORWsvJalBPEVWO7obTA+0kVO9G+6+x+ePDCQZ6X4QW3nVkHAFgwoXuSa5KaJOPbkGr7bImwZ//RyN+xmn1rze9iykoIjU+aS00aGz0z3YQ0JFhzmeqQJo0gCIJIW6REz6LSENYnrWdpXspu8HpyXRk+umUycjPSY2qSaJ+ZZHRvp3vspQKHjjd7lleiow160dzfHohG6mxns3l0qkGaNC2pOVITBEEQhAAyrbza4ksjv710EdDaCukSdCZeJDowkeRB4JA9B6KaxHQLvsFGHk1r0wiPICGNIAiCSFtotdUedqJN7eUdbaEl01lIy/NY4E/0u6O49Er75sAxj2uSODL8UbGExioS0giCIIg0Jo3nkAnDTxsOES5J58igXTpke5pfwoU0l5q0muJcbyuSQLRCWhIrkiKQXQFBpBgDKguSXQWCSBu8MA9q7bAuaLQ6TTghHQOH/PKcfrj/1c+wbPoAbzNOcFO4Hc+un9wTeZn+uESKjTcZzH59NFaRkEYQKcOKa0bj7S++w4yhrW9jZYKIF+wckmQ0Pj5Gk0bzHsIJ6Rg4ZFp9JabVV3qeb6LlVbfjWWF2EIum9Pa0LomC1aSlYdfzHBLSCCJF6FGShx4ltKkyQTiBVlvt8ZNPWqsgGUEgaB/JKIk3d2x7y05aIY3GKhLSCIIgiLRFM3Fqg5MaEdIpumM6IUmJ1d4m8tE9N3ck/rHha8yf0COBpaY26eKTls4EySdNA3kTEwRBEGmLROaOtmijOyaxIkRMnDmwEwCgewICQ/SrKMRNp9TSlggMiV7gcBvdMd24cmwNAOCK0V01IfiTsXl7qkFvH0EQBJG2kPmePdow6tRe6coZA8rRpX02upNZfFJIuJDWNmQ0XDepJ84a2Ak1HXOxY++RyHFaUCIhjSAIgkhjZLJ2tMVPmrRWgSRJGNi5XbKr0WZJ/D5pbQNZliILDxkB8kljIXNHgiAIIm0hTZo9bBvRxIcg3EE+afGHNXdsDrXBBtBBQhpBEASRtmh90uijzsPvI01aPCCBt22R6KfdFiNrstEdjzeFkliT1IDMHQmCIIi0hTaztkcT3ZF80mJmTI+O2PDVXpzYrT1WfZns2hCJIlEy+cvXjsG27w9jQGVhYgpMIVghrbGZhDQS0giCIIhWAclofHwac8ckVqSV8OiPhqAppACh5mRXhUggidKc1nTMRU3H+EfwTEXYNj5OQhqZOxIEQRCtA9Kk8fHLrDN+EivSSpAkCQEfTZ/aApXtspNdhTYLadJIk0YQBEEQrRqfjwKHEIQbCrIDePnaMcgM+OwTE55CPmkkpBEEQRCtBlKl8WDNHSlwCEE4o62aHiabxmYaz0lfTxAEQbQKyNyRDwUOIQgi3SBzRxLSCIIgCKJVQ5tZEwSRbpCQRkIaQRAE0UogRRofWZaQl+mHX5aQFSTfGoIgUh+K7kg+aQRBEATR6vn9RfU4dKwJeZmBZFeFIAjClsYmWnYjIY0gCIJoFZBPmjkndG2f7CoQBEEIQ+aOZO5IEARBtBIUMngkCIJoFRyjEPwkpBEEQRAEQRAEkTqQJo2ENIIgCKKVQOaOBEEQrYOygsxkVyHpkJBGEARBtApIRiMIgkhv/jZ7OMb3KsZ95w1MdlWSDgUOIQiCIFoFhVkUuZAgCCKdqa8qwh9mFSW7GikBadIIgiCItOaX5/TD9PpKnNynJNlVIQiCIAhPSKqQtnTpUgwZMgR5eXkoLi7G1KlT8d///tfymrVr12LkyJFo3749srKy0KtXL9x7772GdH//+99RW1uLjIwM1NbW4umnn9acX716NU477TSUl5dDkiQ888wzhjxmzZoFSZI0/0444YSY7pkgCILwlmn1lbjznH7wyVKyq0IQBEEQnpBUIW3VqlWYM2cO3nrrLaxcuRJNTU2YNGkSDh06ZHpNTk4O5s6di9WrV2Pz5s1YvHgxFi9ejIcffjiS5s0338T06dMxc+ZMbNiwATNnzsS5556Lt99+O5Lm0KFD6N+/P+6//37LOp588snYuXNn5N8LL7wQ+40TBEEQBEEQBEGYkFSftBdffFHz+5FHHkFxcTHWrVuH0aNHc68ZOHAgBg6MOhNWVVVh+fLlWLNmDS6//HIAwLJlyzBx4kQsWrQIALBo0SKsWrUKy5Ytw1/+8hcAwJQpUzBlyhTbOmZkZKC0tNTV/REEQRAEQRAEQTglpQKH7Nu3DwBQVCTuMPjBBx/gjTfewC9+8YvIsTfffBPXXHONJt3kyZOxbNkyx3V67bXXUFxcjMLCQowZMwa33XYbiouLuWmPHTuGY8eORX7v378fANDY2IjGxkbHZXuJWn6y60G0HajPEYmG+hyRSKi/EYmG+lz64+TZpYyQpigKFi5ciFGjRqGurs42fUVFBb755hs0NTVhyZIluPTSSyPndu3ahZISrQN5SUkJdu3a5ahOU6ZMwbRp09ClSxds2bIFN998M0466SSsW7cOGRkZhvRLly7FLbfcYji+YsUKZGdnOyo7XqxcuTLZVSDaGNTniERDfY5IJNTfiERDfS59OXz4sHDalBHS5s6diw8//BBr164VSr9mzRocPHgQb731Fm644QZ069YNM2bMiJyXJK0DuaIohmN2TJ8+PfJ3XV0d6uvr0aVLF/zzn//EWWedZUi/aNEiLFy4MPJ7//79qKysxKRJk5Cfn++obK9pbGzEypUrMXHiRAQCFKaaiD/U54hEQ32OSCTU34hEQ30u/VGt7ERICSFt3rx5eO6557B69WpUVFQIXVNdXQ0A6Nu3L3bv3o0lS5ZEhLTS0lKD1mzPnj0G7ZpTysrK0KVLF3z66afc8xkZGVwNWyAQSJmXKZXqQrQNqM8RiYb6HJFIqL8RiYb6XPri5LklNbqjoiiYO3culi9fjldeeSUieLnJh/UFGz58uEEVvGLFCowYMSKm+n733XfYvn07ysrKYsqHIAiCIAiCIAjCjKRq0ubMmYMnnngCzz77LPLy8iLar4KCAmRlZQEImxDu2LEDjz32GADggQceQOfOndGrVy8A4X3TfvWrX2HevHmRfOfPn4/Ro0fjzjvvxBlnnIFnn30WL730ksaU8uDBg/jss88iv7ds2YL169ejqKgInTt3xsGDB7FkyRKcffbZKCsrw9atW3HjjTeiQ4cOOPPMM+PeNgRBEARBEARBtE2SKqQ9+OCDAICxY8dqjj/yyCOYNWsWAGDnzp3Ytm1b5FwoFMKiRYuwZcsW+P1+1NTU4I477sAVV1wRSTNixAg8+eSTWLx4MW6++WbU1NTgqaeewrBhwyJp3nvvPYwbNy7yW/Ulu/jii/Hoo4/C5/Nh48aNeOyxx7B3716UlZVh3LhxeOqpp5CXl+d1UxAEQRAEQRAEQQBIspCmKIptmkcffVTze968eRqtmRnnnHMOzjnnHNPzY8eOtSw/KysL//73v23LIQiCIAiCIAiC8JKk+qQRBEEQBEEQBEEQWlIiumNrRdXUOQm3GS8aGxtx+PBh7N+/nyICEQmB+hyRaKjPEYmE+huRaKjPpT+qTCBiTUhCWhw5cOAAAKCysjLJNSEIgiAIgiAIIhU4cOAACgoKLNNIiogoR7giFArh66+/Rl5enuONtL1G3Vh7+/btSd9Ym2gbUJ8jEg31OSKRUH8jEg31ufRHURQcOHAA5eXlkGVrrzPSpMURWZaFN+dOFPn5+fRiEwmF+hyRaKjPEYmE+huRaKjPpTd2GjQVChxCEARBEARBEASRQpCQRhAEQRAEQRAEkUKQkNZGyMjIwM9+9jNkZGQkuypEG4H6HJFoqM8RiYT6G5FoqM+1LShwCEEQBEEQBEEQRApBmjSCIAiCIAiCIIgUgoQ0giAIgiAIgiCIFIKENIIgCIIgCIIgiBSChDSCIAiCIAiCIIgUgoS0NsJvf/tbVFdXIzMzE4MHD8aaNWuSXSUiDVm6dCmGDBmCvLw8FBcXY+rUqfjvf/+rSaMoCpYsWYLy8nJkZWVh7Nix+PjjjzVpjh07hnnz5qFDhw7IycnB6aefjq+++iqRt0KkIUuXLoUkSViwYEHkGPU3wmt27NiBCy+8EO3bt0d2djYGDBiAdevWRc5TnyO8pKmpCYsXL0Z1dTWysrLQtWtX/PznP0coFIqkoT7XNiEhrQ3w1FNPYcGCBbjpppvwwQcf4MQTT8SUKVOwbdu2ZFeNSDNWrVqFOXPm4K233sLKlSvR1NSESZMm4dChQ5E0d911F+655x7cf//9ePfdd1FaWoqJEyfiwIEDkTQLFizA008/jSeffBJr167FwYMHceqpp6K5uTkZt0WkAe+++y4efvhh9OvXT3Oc+hvhJT/88ANGjhyJQCCAf/3rX9i0aRPuvvtuFBYWRtJQnyO85M4778RDDz2E+++/H5s3b8Zdd92FX/7yl/jNb34TSUN9ro2iEK2eoUOHKrNnz9Yc69Wrl3LDDTckqUZEa2HPnj0KAGXVqlWKoihKKBRSSktLlTvuuCOS5ujRo0pBQYHy0EMPKYqiKHv37lUCgYDy5JNPRtLs2LFDkWVZefHFFxN7A0RacODAAaV79+7KypUrlTFjxijz589XFIX6G+E9P/3pT5VRo0aZnqc+R3jNKaecovz4xz/WHDvrrLOUCy+8UFEU6nNtGdKktXKOHz+OdevWYdKkSZrjkyZNwhtvvJGkWhGthX379gEAioqKAABbtmzBrl27NP0tIyMDY8aMifS3devWobGxUZOmvLwcdXV11CcJLnPmzMEpp5yCCRMmaI5TfyO85rnnnkN9fT2mTZuG4uJiDBw4EL///e8j56nPEV4zatQovPzyy/jkk08AABs2bMDatWvR0NAAgPpcW8af7AoQ8eXbb79Fc3MzSkpKNMdLSkqwa9euJNWKaA0oioKFCxdi1KhRqKurA4BIn+L1ty+//DKSJhgMol27doY01CcJPU8++STef/99vPvuu4Zz1N8Ir/niiy/w4IMPYuHChbjxxhvxzjvv4Oqrr0ZGRgYuuugi6nOE5/z0pz/Fvn370KtXL/h8PjQ3N+O2227DjBkzANA415YhIa2NIEmS5reiKIZjBOGEuXPn4sMPP8TatWsN59z0N+qThJ7t27dj/vz5WLFiBTIzM03TUX8jvCIUCqG+vh633347AGDgwIH4+OOP8eCDD+Kiiy6KpKM+R3jFU089hT/96U944okn0KdPH6xfvx4LFixAeXk5Lr744kg66nNtDzJ3bOV06NABPp/PsJKyZ88ew6oMQYgyb948PPfcc3j11VdRUVEROV5aWgoAlv2ttLQUx48fxw8//GCahiCAsAnPnj17MHjwYPj9fvj9fqxatQq//vWv4ff7I/2F+hvhFWVlZaitrdUc6927dyTQFo1xhNdcd911uOGGG3Deeeehb9++mDlzJq655hosXboUAPW5tgwJaa2cYDCIwYMHY+XKlZrjK1euxIgRI5JUKyJdURQFc+fOxfLly/HKK6+gurpac766uhqlpaWa/nb8+HGsWrUq0t8GDx6MQCCgSbNz50589NFH1CcJDePHj8fGjRuxfv36yL/6+npccMEFWL9+Pbp27Ur9jfCUkSNHGrYV+eSTT9ClSxcANMYR3nP48GHIsnY67vP5IiH4qc+1YZIUsIRIIE8++aQSCASUP/zhD8qmTZuUBQsWKDk5OcrWrVuTXTUizbjyyiuVgoIC5bXXXlN27twZ+Xf48OFImjvuuEMpKChQli9frmzcuFGZMWOGUlZWpuzfvz+SZvbs2UpFRYXy0ksvKe+//75y0kknKf3791eampqScVtEGsFGd1QU6m+Et7zzzjuK3+9XbrvtNuXTTz9V/vznPyvZ2dnKn/70p0ga6nOEl1x88cVKp06dlOeff17ZsmWLsnz5cqVDhw7K9ddfH0lDfa5tQkJaG+GBBx5QunTpogSDQWXQoEGRkOkE4QQA3H+PPPJIJE0oFFJ+9rOfKaWlpUpGRoYyevRoZePGjZp8jhw5osydO1cpKipSsrKylFNPPVXZtm1bgu+GSEf0Qhr1N8Jr/vGPfyh1dXVKRkaG0qtXL+Xhhx/WnKc+R3jJ/v37lfnz5yudO3dWMjMzla5duyo33XSTcuzYsUga6nNtE0lRFCWZmjyCIAiCIAiCIAgiCvmkEQRBEARBEARBpBAkpBEEQRAEQRAEQaQQJKQRBEEQBEEQBEGkECSkEQRBEARBEARBpBAkpBEEQRAEQRAEQaQQJKQRBEEQBEEQBEGkECSkEQRBEARBEARBpBAkpBEEQRAEQRAEQaQQJKQRBEEQRApQVVWFZcuWJbsaBEEQRApAQhpBEATR5pg1axamTp0KABg7diwWLFiQsLIfffRRFBYWGo6/++67uPzyyxNWD4IgCCJ18Se7AgRBEATRGjh+/DiCwaDr6zt27OhhbQiCIIh0hjRpBEEQRJtl1qxZWLVqFe677z5IkgRJkrB161YAwKZNm9DQ0IDc3FyUlJRg5syZ+PbbbyPXjh07FnPnzsXChQvRoUMHTJw4EQBwzz33oG/fvsjJyUFlZSWuuuoqHDx4EADw2muv4Uc/+hH27dsXKW/JkiUAjOaO27ZtwxlnnIHc3Fzk5+fj3HPPxe7duyPnlyxZggEDBuDxxx9HVVUVCgoKcN555+HAgQPxbTSCIAgi7pCQRhAEQbRZ7rvvPgwfPhyXXXYZdu7ciZ07d6KyshI7d+7EmDFjMGDAALz33nt48cUXsXv3bpx77rma6//4xz/C7/fj9ddfx+9+9zsAgCzL+PWvf42PPvoIf/zjH/HKK6/g+uuvBwCMGDECy5YtQ35+fqS8n/zkJ4Z6KYqCqVOn4vvvv8eqVauwcuVKfP7555g+fbom3eeff45nnnkGzz//PJ5//nmsWrUKd9xxR5xaiyAIgkgUZO5IEARBtFkKCgoQDAaRnZ2N0tLSyPEHH3wQgwYNwu233x459n//93+orKzEJ598gh49egAAunXrhrvuukuTJ+vfVl1djVtvvRVXXnklfvvb3yIYDKKgoACSJGnK0/PSSy/hww8/xJYtW1BZWQkAePzxx9GnTx+8++67GDJkCAAgFArh0UcfRV5eHgBg5syZePnll3HbbbfF1jAEQRBEUiFNGkEQBEHoWLduHV599VXk5uZG/vXq1QtAWHulUl9fb7j21VdfxcSJE9GpUyfk5eXhoosuwnfffYdDhw4Jl79582ZUVlZGBDQAqK2tRWFhITZv3hw5VlVVFRHQAKCsrAx79uxxdK8EQRBE6kGaNIIgCILQEQqFcNppp+HOO+80nCsrK4v8nZOTozn35ZdfoqGhAbNnz8att96KoqIirF27FpdccgkaGxuFy1cUBZIk2R4PBAKa85IkIRQKCZdDEARBpCYkpBEEQRBtmmAwiObmZs2xQYMG4e9//zuqqqrg94t/Kt977z00NTXh7rvvhiyHjVX++te/2panp7a2Ftu2bcP27dsj2rRNmzZh37596N27t3B9CIIgiPSEzB0JgiCINk1VVRXefvttbN26Fd9++y1CoRDmzJmD77//HjNmzMA777yDL774AitWrMCPf/xjSwGrpqYGTU1N+M1vfoMvvvgCjz/+OB566CFDeQcPHsTLL7+Mb7/9FocPHzbkM2HCBPTr1w8XXHAB3n//fbzzzju46KKLMGbMGK6JJUEQBNG6ICGNIAiCaNP85Cc/gc/nQ21tLTp27Iht27ahvLwcr7/+OpqbmzF58mTU1dVh/vz5KCgoiGjIeAwYMAD33HMP7rzzTtTV1eHPf/4zli5dqkkzYsQIzJ49G9OnT0fHjh0NgUeAsNniM888g3bt2mH06NGYMGECunbtiqeeesrz+ycIgiBSD0lRFCXZlSAIgiAIgiAIgiDCkCaNIAiCIAiCIAgihSAhjSAIgiAIgiAIIoUgIY0gCIIgCIIgCCKFICGNIAiCIAiCIAgihSAhjSAIgiAIgiAIIoUgIY0gCIIgCIIgCCKFICGNIAiCIAiCIAgihSAhjSAIgiAIgiAIIoUgIY0gCIIgCIIgCCKFICGNIAiCIAiCIAgihSAhjSAIgiAIgiAIIoX4/3yD9V6/wpsgAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 15
  },
  {
   "cell_type": "code",
   "id": "3e2d6346",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-07T09:41:53.473975Z",
     "start_time": "2025-05-07T09:41:53.410183Z"
    }
   },
   "source": [
    "plt.plot(losses_sgd)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x173925f1670>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 16
  },
  {
   "cell_type": "markdown",
   "id": "1df5b665",
   "metadata": {},
   "source": [
    "接下来使用 Adagrad 优化器（在[官方文档](https://pytorch.org/docs/stable/optim.html)中找到对应的函数），其他参数保持不变，重新训练一次模型，也保存下来损失函数值。"
   ]
  },
  {
   "cell_type": "code",
   "id": "0e8381b0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-07T09:43:41.266951Z",
     "start_time": "2025-05-07T09:43:24.957883Z"
    }
   },
   "source": [
    "\n",
    "nepoch = 3\n",
    "batch_size = 200\n",
    "lr = 0.001\n",
    "\n",
    "np.random.seed(123)\n",
    "torch.manual_seed(123)\n",
    "\n",
    "model = MyModel()\n",
    "loss_fn = torch.nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adagrad(model.parameters(), lr=lr) \n",
    "losses_adagrad = []\n",
    "\n",
    "train_dataset = torch.utils.data.TensorDataset(x, y)\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "\n",
    "for epoch in range(nepoch):\n",
    "    for batch_x, batch_y in train_loader:\n",
    "        pred = model(batch_x)\n",
    "        loss = loss_fn(pred, batch_y)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        losses_adagrad.append(loss.item())\n",
    "\n",
    "    print(f\"Epoch {epoch + 1}/{nepoch} ，Loss: {loss.item():.4f}\")\n",
    "\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.plot(losses_adagrad)\n",
    "plt.title(\"Adagrad Training Loss Curve\")\n",
    "plt.xlabel(\"Iteration\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3 ，Loss: 1.5848\n",
      "Epoch 2/3 ，Loss: 1.5358\n",
      "Epoch 3/3 ，Loss: 1.5249\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 18
  },
  {
   "cell_type": "markdown",
   "id": "c00718ca",
   "metadata": {},
   "source": [
    "对比 SGD 和 Adagrad，画出各自的损失函数曲线。"
   ]
  },
  {
   "cell_type": "code",
   "id": "3eb0e375",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-07T09:43:44.481420Z",
     "start_time": "2025-05-07T09:43:44.423733Z"
    }
   },
   "source": [
    "plt.plot(losses_sgd)\n",
    "plt.plot(losses_adagrad)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1738f9060c0>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 19
  },
  {
   "cell_type": "markdown",
   "id": "2b382b90",
   "metadata": {},
   "source": [
    "最后再自行选择一款优化器，重复上面的实验，并画出三种优化器的损失函数值对比图。"
   ]
  },
  {
   "cell_type": "code",
   "id": "ffc244f3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-07T09:45:26.222899Z",
     "start_time": "2025-05-07T09:45:08.184830Z"
    }
   },
   "source": [
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "nepoch = 3\n",
    "batch_size = 200\n",
    "lr = 0.001\n",
    "\n",
    "np.random.seed(123)\n",
    "torch.manual_seed(123)\n",
    "\n",
    "# 使用 Adam 优化器\n",
    "model = MyModel()\n",
    "loss_fn = torch.nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
    "losses_adam = []\n",
    "\n",
    "# 准备数据加载器\n",
    "train_dataset = torch.utils.data.TensorDataset(x, y)\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "\n",
    "# 训练循环\n",
    "for epoch in range(nepoch):\n",
    "    for batch_x, batch_y in train_loader:\n",
    "        pred = model(batch_x)\n",
    "        loss = loss_fn(pred, batch_y)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        losses_adam.append(loss.item())\n",
    "\n",
    "    print(f\"Epoch {epoch + 1}/{nepoch}，Loss: {loss.item():.4f}\")\n",
    "\n",
    "plt.figure(figsize=(12, 5))\n",
    "plt.plot(losses_sgd, label=\"SGD\")\n",
    "plt.plot(losses_adagrad, label=\"Adagrad\")\n",
    "plt.plot(losses_adam, label=\"Adam\")\n",
    "plt.title(\"Loss Curve Comparison: SGD vs Adagrad vs Adam\")\n",
    "plt.xlabel(\"Iteration\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3，Loss: 1.5100\n",
      "Epoch 2/3，Loss: 1.4656\n",
      "Epoch 3/3，Loss: 1.4690\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 20
  },
  {
   "cell_type": "markdown",
   "id": "411e0bfe",
   "metadata": {},
   "source": [
    "### 2.6 实施预测"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cc09b3f",
   "metadata": {},
   "source": [
    "为了验证模型的效果，我们对10个测试观测（即之前生成的 `testx`）进行预测。"
   ]
  },
  {
   "cell_type": "code",
   "id": "80a99a6e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-07T09:46:14.778045Z",
     "start_time": "2025-05-07T09:46:14.769479Z"
    }
   },
   "source": [
    "ypred = model(xtest)\n",
    "print(np.round(ypred.detach().cpu().numpy(), 3))\n",
    "print(ytest)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.   0.   0.   0.   1.   0.   0.   0.   0.   0.  ]\n",
      " [0.   1.   0.   0.   0.   0.   0.   0.   0.   0.  ]\n",
      " [0.   0.   1.   0.   0.   0.   0.   0.   0.   0.  ]\n",
      " [0.   0.   0.   0.   0.   0.   0.   1.   0.   0.  ]\n",
      " [0.   0.   0.   0.   0.   0.   1.   0.   0.   0.  ]\n",
      " [1.   0.   0.   0.   0.   0.   0.   0.   0.   0.  ]\n",
      " [0.   0.   0.79 0.21 0.   0.   0.   0.   0.   0.  ]\n",
      " [0.   0.   0.   0.   0.   0.   0.   1.   0.   0.  ]\n",
      " [0.   0.   0.   0.   1.   0.   0.   0.   0.   0.  ]\n",
      " [0.   0.   0.   0.   0.   0.   1.   0.   0.   0.  ]]\n",
      "tensor([4, 1, 2, 7, 6, 0, 3, 7, 4, 6])\n"
     ]
    }
   ],
   "execution_count": 21
  },
  {
   "cell_type": "markdown",
   "id": "38c93ff3",
   "metadata": {},
   "source": [
    "如果模型搭建和训练都正常，那么每一行中概率最大的取值所在的位置应该正好对应真实的标签。我们也可以让 PyTorch 自动找到最大值的位置。"
   ]
  },
  {
   "cell_type": "code",
   "id": "e7bd7b9b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-07T09:46:18.540378Z",
     "start_time": "2025-05-07T09:46:18.532907Z"
    }
   },
   "source": [
    "torch.argmax(ypred, dim=1)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([4, 1, 2, 7, 6, 0, 2, 7, 4, 6])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 22
  },
  {
   "cell_type": "markdown",
   "id": "6c34de42",
   "metadata": {},
   "source": [
    "最后，我们用模型对一些真实的手写数字图片进行预测。请你利用绘图软件（如 Windows 自带的绘图，或 Photoshop 等）准备10张正方形黑色底色的图片，每张用鼠标绘制一个数字（**请使用较粗的笔划**），从0到9，然后以0.png，1.png等文件名存储下来，放到当前目录一个名为 digits 的文件夹中。以下是几个例子：\n",
    "![](digits_old/sample0.png) ![](digits_old/sample5.png) ![](digits_old/sample8.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35066889",
   "metadata": {},
   "source": [
    "接下来利用 Pillow 软件包读取图片："
   ]
  },
  {
   "cell_type": "code",
   "id": "0d19fbd2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-07T10:06:32.051295Z",
     "start_time": "2025-05-07T10:06:32.046978Z"
    }
   },
   "source": [
    "from PIL import Image\n",
    "im = Image.open(\"digits/0.png\")\n",
    "im"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=L size=28x28>"
      ],
      "image/png": "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",
      "image/jpeg": "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"
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 34
  },
  {
   "cell_type": "markdown",
   "id": "7f30128c",
   "metadata": {},
   "source": [
    "此时如果直接将其转为 Numpy 数组会得到三个通道："
   ]
  },
  {
   "cell_type": "code",
   "id": "f3999832",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-07T10:06:34.875313Z",
     "start_time": "2025-05-07T10:06:34.870636Z"
    }
   },
   "source": [
    "im_arr = np.array(im)\n",
    "print(im_arr.shape)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n"
     ]
    }
   ],
   "execution_count": 35
  },
  {
   "cell_type": "markdown",
   "id": "06d85450",
   "metadata": {},
   "source": [
    "因此，我们先强制转换为灰度图片（单通道），再缩放至模型的图片大小 28 x 28："
   ]
  },
  {
   "cell_type": "code",
   "id": "102618e8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-07T10:06:51.403577Z",
     "start_time": "2025-05-07T10:06:51.397992Z"
    }
   },
   "source": [
    "im = im.convert(\"L\")\n",
    "im.thumbnail((28, 28))\n",
    "im_arr = np.array(im)\n",
    "print(im_arr.shape)\n",
    "im"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<PIL.Image.Image image mode=L size=28x28>"
      ],
      "image/png": "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",
      "image/jpeg": "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"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 36
  },
  {
   "cell_type": "markdown",
   "id": "3978a9d6",
   "metadata": {},
   "source": [
    "为了传递给模型对象，还需要先将数值归一化到 [0,1] 区间，转换为 PyTorch 的 Tensor 类型，并增加一个批次和一个通道的维度："
   ]
  },
  {
   "cell_type": "code",
   "id": "ce6af013",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-07T10:06:57.177665Z",
     "start_time": "2025-05-07T10:06:57.174385Z"
    }
   },
   "source": [
    "test0 = torch.tensor(im_arr / 255.0, dtype=torch.float32).view(1, 1, 28, 28)\n",
    "print(test0.shape)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 1, 28, 28])\n"
     ]
    }
   ],
   "execution_count": 37
  },
  {
   "cell_type": "markdown",
   "id": "69402359",
   "metadata": {},
   "source": [
    "最后对图片标签进行预测："
   ]
  },
  {
   "cell_type": "code",
   "id": "28b1f088",
   "metadata": {
    "scrolled": true,
    "ExecuteTime": {
     "end_time": "2025-05-07T10:07:02.276613Z",
     "start_time": "2025-05-07T10:07:02.267600Z"
    }
   },
   "source": [
    "pred0 = model(test0)\n",
    "print(np.round(pred0.detach().cpu().numpy(), 3))"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.    0.    0.009 0.003 0.    0.    0.    0.006 0.001 0.981]]\n"
     ]
    }
   ],
   "execution_count": 38
  },
  {
   "cell_type": "markdown",
   "id": "6ebafef2",
   "metadata": {},
   "source": [
    "预测结果是否符合真实情形？请对你自己绘制出的10张图片进行类似的预测操作，并评价其效果。"
   ]
  },
  {
   "cell_type": "code",
   "id": "9fadb617",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-07T10:10:29.306149Z",
     "start_time": "2025-05-07T10:10:29.021359Z"
    }
   },
   "source": [
    "import os\n",
    "from PIL import Image\n",
    "import torch\n",
    "import torchvision.transforms as transforms\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "img_dir = \"digits\"\n",
    "img_files = [f\"{i}.png\" for i in range(10)]\n",
    "\n",
    "model.eval()\n",
    "\n",
    "transform = transforms.Compose([\n",
    "    transforms.Grayscale(),\n",
    "    transforms.Resize((28, 28)),\n",
    "    transforms.ToTensor()\n",
    "])\n",
    "\n",
    "correct = 0\n",
    "plt.figure(figsize=(12, 3))\n",
    "for idx, file in enumerate(img_files):\n",
    "    path = os.path.join(img_dir, file)\n",
    "\n",
    "    image = Image.open(path).convert(\"RGB\")\n",
    "    input_tensor = transform(image).unsqueeze(0)  \n",
    "\n",
    "    with torch.no_grad():\n",
    "        output = model(input_tensor)\n",
    "        pred = torch.argmax(output, dim=1).item()\n",
    "\n",
    "    plt.subplot(2, 5, idx + 1)\n",
    "    plt.imshow(image, cmap=\"gray\")\n",
    "    plt.title(f\"prediction:{pred}\")\n",
    "    plt.axis(\"off\")\n",
    "\n",
    "    if pred == idx:\n",
    "        correct += 1\n",
    "\n",
    "plt.suptitle(f\"accuracy:{correct}/10\", fontsize=16)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x300 with 10 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 44
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
